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Token Economics and AI Compute Costs: A Comprehensive Analysis

Using SCHOOL (Pty) Ltd as a Primary Case Study

Executive Summary

This whitepaper examines the economic foundations of modern AI systems, focusing on the critical interplay between token economics and computational infrastructure costs. Using SCHOOL (Pty) Ltd as a case study, we analyze how compute allocation, inference economics, and token economics determine the viability and scalability of AI ventures in today's market.

Table of Contents

Part I: Fundamentals & Foundations

• Chapter 1: Defining the Modern LLM Landscape
• Chapter 2: The Compute Bottleneck
• Chapter 3: Model Taxonomy and Architecture Review

Part II: The Economics of Intelligence

• Chapter 4: Training Costs and Capital Expenditure
• Chapter 5: Inference Optimization
• Chapter 6: Ecosystem Effects

Part III: Future Trajectories & Societal Impact

• Chapter 7: Advanced Architectures
• Chapter 8: Regulatory Risk and Governance
• Chapter 9: Conclusion & Roadmap

Additional Sections

• History of AI Evolution
• History of Open Source AI
• Terminal Development History
• Cloud Infrastructure Comparison
• Model Comparison Analysis
• Token Economics Deep Dive
• SCHOOL Case Study
• Building a Claude-Class System
• Strategic Recommendations

Chapter 1: Defining the Modern LLM Landscape

The Language Model Economy: What, Why, and For Whom

1.1 Introduction to the Language Model Economy

Large language models are no longer a research curiosity; they are an industrial input. Every day, millions of software products, search systems, writing tools, and educational platforms route a portion of their work through a transformer. Each routed request consumes a predictable quantity of compute, returns a predictable quantity of tokens, and incurs a predictable quantity of cost. That predictability is the foundation on which the entire economic analysis in this paper rests.

This chapter defines the landscape in which those costs are incurred: what a language model is, how the modern market is structured, which actors compete at which layer, and why token economics — the unit economics of a single generated word — has become the common language of the industry.

The chapter is structured as follows:

• Defining the artifact: what a large language model actually is, in economic terms
• The token as the unit of account: why the industry prices by tokens rather than by queries or seconds
• Market structure: the layer cake of hardware, foundation models, fine-tunes, and applications
• The South African dimension: where SCHOOL (Pty) Ltd sits in this landscape and what the landscape means for an African venture

Throughout this paper, SCHOOL (Pty) Ltd is used as the organizing case study. SCHOOL is an AI-powered education platform serving South African learners from Grade R through postgraduate study. It is not a laboratory building foundation models; it is an application-layer venture that must buy, consume, and optimize tokens in a market shaped by a handful of frontier laboratories. Understanding the landscape in Chapter 1 determines which costs SCHOOL can control and which it can only optimize around.

1.2 What a Large Language Model Is

1.2.1 The Transformer and the Autoregressive Loop

A language model is a statistical machine that assigns probabilities to sequences of tokens. The dominant architecture deployed today is the transformer, introduced in 2017, which processes sequences through stacked layers of self-attention. During generation, a model operates in an autoregressive loop: it reads the tokens already produced, predicts the next token, appends it to the context, and repeats. The output length of a conversation is therefore a sum of individual prediction steps, each of which requires the model to process the entire growing context.

This loop has three direct economic consequences:

• Cost scales with output length: every generated token is itself a full forward pass over the context
• Cost scales with context length: the same forward pass grows more expensive as the conversation grows
• Latency compounds: the response time of a model is the sum of per-token latencies, not a single computation

A model that appears identical by headline parameter count can therefore differ dramatically in cost per useful answer, depending on how its inference stack deploys attention, caching, and batching. These mechanics are examined in depth in Chapters 2 and 5.

1.2.2 The Parameter as an Economic Variable

Model capability is approximated, imperfectly, by parameter count. Frontier systems of the mid-2020s are measured in the hundreds of billions of parameters; open-weight and student-grade systems at the tens of billions. Predictably, the relationship between size and cost is not linear:

• Training cost scales with parameter count and token count: doubling either roughly doubles the compute of a training run
• Inference cost scales with parameter count per token: every token generation touches every parameter through at least one forward pass
• Memory scales with parameter count: weights must be resident in high-bandwidth memory for both training and inference

The economically important insight for an application-layer venture is that capability gains from scale are subject to diminishing returns relative to their cost. For many controlled tasks — mathematical tutoring, essay feedback, curriculum-aligned explanation — models in the 7B–34B parameter range frequently match frontier models at a fraction of the per-token cost. Chapter 3 turns that observation into a taxonomy; this chapter merely establishes that parameter count is a cost variable first and a capability variable second.

1.2.3 The Token as the Unit of Account

Why does the industry price in tokens rather than queries? Three reasons dominate:

• Granularity: a query can cost milliseconds or minutes of compute; none of that variation is visible at the query level
• Proportionality: token count tracks actual compute consumption more closely than any user-facing unit
• Billing compatibility: input tokens, output tokens, cached tokens, and tool-use tokens can be priced at different rates, allowing providers to express complex cost structures precisely

For SCHOOL, token pricing has a direct pedagogical consequence. If the platform prices its free tier in absolute token credits per student per day, then the token is simultaneously a cost unit (what SCHOOL pays) and a product unit (what the student receives). The unit economics of a single tutoring session collapse to tokens consumed per learning outcome — a measure elaborated throughout this paper.

Impact on Token Economics Treating the token as the unit of account allows granular cost attribution: which subjects are expensive, which prompt patterns waste tokens, which students require retries. Without token-level accounting, optimization is impossible; with it, every improvement in prompt design, caching, or model selection is directly measurable in currency terms.

1.3 The Layered Market Structure

1.3.1 The Layer Cake

The modern AI market separates into distinct layers, each with its own cost base and competitive dynamics:

• Hardware: GPUs, TPUs, and accelerators, plus the datacenters, power, and cooling that house them
• Compute providers: hyperscalers and independent clouds that convert silicon into rented capacity
• Foundation model providers: laboratories that train frontier-scale models and expose them as APIs
• Model operators: teams that run open-weight models on rented or owned capacity
• Application builders: ventures like SCHOOL that consume model output to deliver end-user value

Each layer extracts margin from the layer below. The price a venture pays per token is the sum of the margins of every layer beneath it — hardware amortization, compute markups, training cost recovery, and provider margin. The practical implication is that a venture's token price is largely exogenous, set by actors above it in the stack, while the token consumption and required quality are endogenous. This asymmetry defines the optimization problem that Chapters 4 and 5 address.

1.3.2 Frontier vs. Open-Weight vs. Fine-Tuned

The foundation layer itself divides into three economic categories:

Frontier (closed) models

• Trained at $10M to $100M+ per run
• Exposed only through paid APIs
• Highest capability at the highest per-token price
• Zero marginal capital cost for the consumer, but persistent usage cost

Open-weight models

• Weights published for reuse
• Community-driven fine-tuning and evaluation
• Self-hostable: marginal cost moves from API fees to compute ownership
• Quality typically trails the frontier but leads on price and data control

Fine-tuned models

• Open-weight or API-accessible models adapted to a domain
• Cost concentrated in data preparation and a modest fine-tuning run
• Often superior to frontier models on the narrow task for which they are adapted

The strategic choice for SCHOOL is not between these categories but across them: prompt-engineering across frontier models for the highest-stakes tutoring interactions, routing routine workloads to fine-tuned open-weight models, and reserving small models for low-stakes classification and retrieval. The economics of this routing are the subject of Chapter 5.

1.3.3 The Cognitive Value Chain

AI applications convert token spend into user value through a well-defined chain:

1. Input: user state, curriculum data, and instructions are assembled into a prompt
2. Computation: the model maps the prompt to an output distribution
3. Output: tokens are generated, filtered, and surfaced
4. Validation: outputs are checked against correctness, safety, and policy criteria
5. Learning: validation results feed back into prompts, fine-tunes, and product design

Cost leaks at every stage. Poor prompts produce verbose outputs that consume tokens without adding value. Weak validation lets incorrect outputs through to users, requiring retry loops. Absent feedback, the same expensive mistakes repeat indefinitely. A venture that manages the full chain controls its effective price per useful token; a venture that only manages step 2 is paying for whatever the model chooses to emit. SCHOOL's approach — structured prompts, curriculum-aligned evaluation, human-in-loop teacher validation — is a direct attempt to compress the chain and convert token spend into measurable learning outcomes.

1.4 The Entropy of Educational Misconceptions and the "Cognitive Friction" Coefficient

1.4.1 Why Learner Confusion Is an Economic Variable

Educational AI has an economic property that commodity applications lack: the entropy of learner misconceptions. A learner's current knowledge state is uncertain, and the space of possible misconceptions is large. When a model responds to a confused learner, it is effectively sampling from a distribution it has not fully observed. High-entropy states produce longer, more exploratory, and more expensive responses — and a higher probability of the model reinforcing the very misconception it means to correct.

This is not a quality observation; it is a cost observation. The same student, with the same tutor product, will consume materially different token volumes depending on how far their mental model has drifted from the curriculum. Two students sitting in the same classroom can have different marginal costs per learning outcome, purely as a function of uncertainty about what they believe.

1.4.2 The Cognitive Friction Coefficient

Define the Cognitive Friction Coefficient as a measure of the resistance a learner experiences between their current conceptual state and the target state:

• Friction depends on conceptual distance: gaps, not grades, drive the difficulty of remediation
• Friction drives token spend: each unit of unresolved friction translates into additional explanation, probing, and validation tokens
• Friction is diagnosable: misconception recovery, not final answer quality, predicts the cost of the next interaction

The practical application for SCHOOL is a pre-flight estimation step: given a grade, a subject, and a diagnostic probe, estimate the friction coefficient before committing tokens. High-friction sessions are routed to the strongest model with the richest context; low-friction sessions are served by small, fast, cheap models. Token spend is thus allocated in proportion to conceptual entropy — an application of the model-routing logic formalized in Chapter 5.

1.4.3 Measuring the Coefficient From Interaction Data

Friction is not a theoretical construct; it is measurable from the traces SCHOOL already generates:

• Probe variance: the number of diagnostic questions required to localize a misconception
• Revision cycles: iterations before a learner produces a correct, self-generated answer
• Assistance gradient: how much scaffolding and hinting each revision required
• Retention: whether the corrected misconception survives a spaced-repetition check

Aggregating these signals per subject, per grade, and per concept produces a friction map of the curriculum. The map is an operational asset: it tells SCHOOL where token spend is structurally inflated and where targeted content changes would compress it. In thermodynamic terms developed in the appendices, it is the entropy inventory of the learning system.

Impact on Token Economics By pricing misconceptions explicitly, an educational platform converts an intangible — student confusion — into a budgetable line item. The Cognitive Friction Coefficient links pedagogical design to token spend, making curriculum planning an economic activity rather than a purely educational one. This single reframing is what allows SCHOOL to treat its token budget as a strategic allocation rather than a variable annoyance.

Chapter 1 Summary

Chapter 2: The Compute Bottleneck

Memory, Bandwidth, and the Physics of the Token

2.1 Introduction to the Compute Bottleneck

• Language models are an industrial input priced by the token; unit economics dominate strategy
• Capability tracks parameter count only loosely, and never linearly with cost
• The token is the unit of account because it prices actual consumption granularly
• The market is a layer cake; application builders price-take from the layers above
• Models divide into frontier, open-weight, and fine-tuned, with distinct cost profiles
• Learner confusion is measurable and priceable via the Cognitive Friction Coefficient
• Token spend should be allocated in proportion to conceptual entropy

The dominant popular narrative about AI cost is that compute — FLOPs — is the scarce and expensive resource. This narrative is only half true. At inference time, the binding constraint is not arithmetic but memory bandwidth: how fast a chip can move model weights from memory into the compute units that multiply them. A token generation is not limited by how fast a GPU can multiply; it is limited by how fast it can stream several billion parameters per token step.

Underneath every figure in this paper sits a thermodynamic absolute: the Landauer limit, the minimum energy required to erase one bit of information. Real systems burn many orders of magnitude above the limit, but the limit's existence fixes a floor beneath compute economics and explains why the prime input to intelligence is energy. AI inference and digital commodity mining (Bitcoin) are, at bottom, the same purchase — energy converted into computed proof. That is the dynamic that makes the compute bottleneck a financial statement rather than an engineering footnote, and it is the reason the energy-intelligence arbitrage in section 2.4 is a strategic weapon rather than a cost-saving trick.

Chapter 2 makes the case that the compute bottleneck is structural, that it shapes both price and strategy, and that a venture like SCHOOL can neither ignore it nor directly relieve it — but can route around it. The chapter covers:

• FLOPS vs. memory bandwidth: why the arithmetic ceiling is not the real ceiling
• The memory-bandwidth wall: what limits token generation throughput
• Hardware economics: silicon, datacenters, power, and the cost stack
• The energy-intelligence arbitrage loop: why geography is a financial instrument

Throughout, the numbers follow the knowledge base established in this paper: training runs at $10M–$100M+, geographic energy arbitrage worth 50–80%, and memory bandwidth as the primary constraint on inference.

2.2 The Arithmetic Ceiling vs. the Real Ceiling

2.2.1 FLOPS and the Public Enthusiasm

Vendors quote arithmetic throughput as if it were the constraint that matters. A flagship accelerator advertises tens of peta-FLOPs. At that arithmetic rate, generating a thousand tokens should take microseconds. In practice it takes seconds. The discrepancy is instructive: the advertised number assumes the data is already in the multiply-accumulate units, when in fact the data is in memory and the transfer is the bottleneck.

This chapter is explicit on the point: for transformer generation, every token produced requires streaming the entire weight set — all parameters — through the memory subsystem once per forward pass. If a model has 13 billion parameters and each parameter is two bytes, a single token step streams roughly 26 gigabytes of weights. The cost of that transfer, divided by the available memory bandwidth, sets the minimum latency of a single token — independent of how fast the arithmetic units could in principle run.

2.2.2 The Utilization Gap

The arithmetic units of inference hardware are starved. Utilization of peak FLOPs during autoregressive generation is frequently in the single-digit percentages precisely because the memory subsystem cannot feed weights fast enough. This gap matters economically in two directions:

• Providers under-provision effective capacity: advertised FLOPs do not translate into revenue-generating tokens
• Consumers pay for unrealized arithmetic: the price per token must recover the silicon purchased for arithmetic that was never performed

The gap is not a software bug; it is a structural property of autoregressive decoding. It is the reason the industry's optimization efforts — quantization, distillation, speculative decoding, efficient attention — all target the memory path rather than the arithmetic path.

2.2.3 Why the Gap Favors Small Models

Because per-token latency is dominated by weight streaming, the model with fewer parameters is fundamentally cheaper per token at equivalent hardware:

• A 7B model streams ~14GB per token step; a 70B model streams ~140GB
• At equal bandwidth, the 7B model generates tokens roughly ten times faster
• Latency is proportional to parameter count, not to task complexity

This is the physical basis for the model-sizing thesis of this paper: for a fixed inference hardware, tokens per second scales inversely with parameter count, and a 7B–34B model that produces an acceptable answer is not merely preferable to a frontier model on price — it is preferable on physical throughput. Chapter 3 catalogs which tasks can be acceptably served at which scale.

2.3 The Memory-Bandwidth Wall

2.3.1 Anatomy of a Token Step

The cost anatomy of a single generated token, in order of magnitude:

• Weight streaming: every parameter read from high-bandwidth memory — the dominant cost
• Attention computation: the KV-cache exchange over the context — the second-order cost
• Arithmetic: the multiply-accumulate work — substantial at training, minor at inference
• Communication: tensor and pipeline transfers across chips in a multi-device setup

Each term has a distinct scaling law. Weight streaming scales with parameter count. Attention scales with context length (and quadratically in the naive formulation). Communication scales with model size and device count. A venture cannot change these scaling laws, but it can choose which to incur: small models shrink weight streaming, context management shrinks the attention term, and single-device deployment avoids communication entirely.

2.3.2 The Combination That Binds

The bind is that the three largest terms compound simultaneously. A frontier model on a long context incurs heavy weight streaming, and its attention term grows with the very conversation length that made the streaming necessary. The result is an inference stack in which every axis of the problem is large at once. Providers relieve the bind through batching (amortizing weight streaming across many users), caching (paying the attention term once instead of per step), and quantization (shrinking the streaming volume). These mitigations are the business of Chapter 5.

2.3.3 Measuring the Wall in Tokens

Measuring the memory wall is straightforward in production:

• Tokens per second per GPU: the headline throughput number
• Tokens per watt: throughput normalized to energy
• Tokens per byte streamed: an efficiency ratio that isolates algorithmic quality from hardware
• Effective tokens per dollar: the number that actually governs product economics

For SCHOOL, the operational target is not raw throughput but effective tokens per dollar at a grade-appropriate quality bar. Progress is measured on that single ratio — a ratio that improved 2–10x through software and algorithmic efficiency alone, per the knowledge base of this paper, before any hardware purchase is considered.

2.4 The Energy-Intelligence Arbitrage Loop and Geographic Moats

2.4.1 Power Is the Long-Term Constraint

Compute is often priced as if silicon were the constraint; silicon is recyclable and replaceable. Power is not. A datacenter's lifetime electricity bill typically dwarfs its hardware bill, and an LLM training run or a large inference fleet is, at bottom, an arbitrage between electricity and intelligence. Every token is a packet of energy converted into predicted probability mass. The economic question is what that conversion costs.

The knowledge base of this paper records the figures: geographic energy optimization yields 50–80% reduction in the energy component of compute cost. That spread is larger than most model-selection savings and is captured with zero algorithmic risk — it is pure site engineering.

2.4.2 Where Energy Is Cheap

Energy price is a function of generation mix, regulation, and geography:

• Hydro and nuclear regions offer stable baseload at low marginal cost
• Solar-rich regions offer daytime generation whose price is approaching zero at peak
• Stranded renewables — generation that would otherwise be curtailed — are the cheapest electricity on earth
• Cooling climates reduce the energy consumed by heat removal, compounding the saving

The 50–80% band in the knowledge base reflects the spread between an unoptimized urban site and a cold, renewable-rich, hydro-stable region. Capturing it requires locating or contracting compute where the arbitrage exists.

2.4.3 The Loop That Creates the Moat

The energy-intelligence arbitrage loop operates as follows:

1. Locate compute where energy is cheap
2. Convert cheap energy into trained or served intelligence
3. Sell intelligence at the global token price
4. Reinvest the margin in more compute, deeper optimization, or better models

Each pass through the loop widens the gap: the venture that enjoys a 50–80% energy discount can under-price, over-invest, or fund talent that competitors with ordinary energy costs cannot. The moat compounds, because the discount applies to every token the venture ever serves.

2.4.4 Applying the Loop in an Emerging-Market Context

For SCHOOL, the arbitrage is indirect but real. The platform must buy tokens priced on whatever infrastructure the market offers, so its exposure to energy cost is the provider's, not its own. The strategic move is therefore not to build datacenters but to control the layer closest to the discounted energy:

• Contract energy-backed compute capacity when the spread justifies it
• Weight the routing logic toward providers whose energy mix is cheapest and most stable
• Time-shift non-interactive workloads (dataset preparation, fine-tuning, batch evaluation) toward periods of cheapest surplus power
• Track the energy mix of every provider as a first-class cost input

The loop turns SCHOOL from a pure token price-taker into a marginal energy arbitrageur — capturing the 50–80% discount on the share of workloads it can schedule, without owning a single megawatt.

Impact on Token Economics Memory bandwidth fixes the floor of per-token latency; energy fixes the floor of per-token cost. A venture that accepts both floors as exogenous permanently overpays. By selecting small models against the bandwidth wall and cheap-energy capacity against the power floor, the same learning outcomes can be delivered at a fraction of the delivered cost — the arithmetic behind SCHOOL's commitment to serving its market at data-light, cost-light economics.

Chapter 2 Summary

Chapter 3: Model Taxonomy and Architecture Review

The Architecture of the Intelligent Artifact

3.1 Introduction to Model Taxonomy

• Arithmetic capacity is not the real constraint; memory bandwidth is
• Each generated token requires streaming all weights from memory once
• Per-token cost scales with parameter count at fixed hardware
• Attention and communication costs compound the weight-streaming bind
• Power, not silicon, is the long-term economic constraint
• Geographic energy arbitrage is worth 50–80% of the energy component
• The arbitrage loop compounds into a durable cost moat
• Even token price-takers can capture energy savings on schedulable workloads

Chapter 1 established that models are industrial inputs priced by the token; Chapter 2 established the physical constraints that govern per-token cost. Chapter 3 closes the loop by cataloging the artifact itself: the architectures, model families, and deployment tiers among which a venture actually chooses. Taxonomy is not an academic exercise here — every architectural family has a distinct cost signature, and the correct choice determines whether a given feature lives or dies on unit economics.

This chapter proceeds in three movements:

3.2 A Taxonomy of the Deployed Landscape

3.2.1 Dense Transformer Models

• A taxonomy of the deployed landscape: dense, sparse (MoE), and multimodal families
• The variance problem: the cost of reliability in a stochastic artifact
• The macro-economics of the educational value equation: how SCHOOL prices the entire model portfolio against learning outcomes

The default architecture: every parameter participates in every forward pass. Dense models are the most predictable and the best understood:

• Uniform cost: every token touches every parameter, so cost scales cleanly with model size
• Best weight-to-capability ratio at small scale: a well-trained dense 7B–13B model is remarkably capable per unit of memory
• Simple deployment: single-device or small-cluster inference, minimal communication overhead
• Primary weakness: capability plateaus; pushing further requires quadratic-computing-inefficient scale

For SCHOOL, small dense models are the workhorses of routine tasks: classification, extraction, formatting, and low-stakes generation where a frontier model buys nothing.

3.2.2 Sparse (Mixture-of-Experts) Models

Mixture-of-Experts (MoE) architectures activate only a subset of parameters per token, routing each input to the expert weights most relevant to it:

• Capability of a large total parameter count with the runtime cost of a smaller active count
• Active parameters per token are a fraction of total parameters, changing the streaming math from Chapter 2
• Routing overhead: a router must classify each token, adding a small fixed cost
• Deployment complexity: experts must be sharded across devices, raising communication costs
• Price-per-token advantage: at equal capability, MoE often undercuts dense on inference cost

The economic attraction of MoE is that it relaxes the Chapter 2 bind: the memory stream per token is governed by active parameters, not total parameters. This is the reason MoE has become the workhorse of frontier deployment economics and why a venture evaluating self-hosted options should read MoE claims carefully — capability is advertised at total size, cost is incurred at active size, and the gap is the prize.

3.2.3 Multimodal Models

Models that consume or produce images, audio, and video in addition to text:

• Heavier encoders: perception layers add compute above the language stack
• Higher input cost: images and audio tokenize into many more tokens than their descriptive text
• Higher output cost: image generation and speech synthesis are expensive per unit of "content"
• Strategic value: the ability to reason over diagrams, plots, and recorded lectures is core to education

For SCHOOL, multimodality is most valuable in the input direction — a learner submits a photograph of class notes or a geometry diagram, and the tutor must parse it. The economics favor sending perception-heavy inputs to frontier multimodal APIs occasionally, while keeping language-only tutoring on cheaper models. Architecture selection thus becomes a routing decision across modalities, not a single model decision.

3.2.4 The Taxonomy in One Table

• Dense small (1B–13B): cheapest, fastest, adequate for classification and structured tasks
• Dense mid (13B–34B): the quality-per-dollar sweet spot for open-weight tutoring
• Dense large (34B–100B+): strong quality, heavy memory streaming, multi-device deployment
• MoE (large total, small active): frontier-adjacent quality at reduced per-token cost
• Multimodal: indispensable where perception is needed, expensive per unit of content

The chapter's thesis follows directly from Chapters 1 and 2: because cost tracks active parameters per token, a venture should match task complexity to the smallest architecture that meets the quality bar. The residual tasks — high-friction, high-stakes, perception-heavy — are routed upward.

3.2.5 The Variance of Outputs and the Cost of Reliability

3.2.5.1 Why Variance Is Priced

Language models are stochastic; identical prompts do not produce identical outputs. Reliability has an explicit price:

• Sampling temperature trades determinism for creativity
• Constrained decoding (grammars, schemas) removes the need for post-hoc validation at the cost of throughput
• Retry economics: the expected number of attempts to get one passing output multiplies the token cost
• Verifier costs: every output that requires external checking consumes non-model compute and often human attention

The reliable-cost of a workload is therefore the price of one attempt times the mean attempts-to-success, plus verification. Ventures routinely underprice this because they budget single attempts. SCHOOL's correction is to price the distribution, not the point: for a curriculum-critical explanation, the correct budget is the expected cost of whatever sequence of generation and validation reliably yields a correct explanation.

3.2.5.2 The Variance-Routing Rule

Because reliability costs differ across architectures, the cheapest reliable model for a task is rarely the cheapest average model:

• High variance, high stakes (final feedback to a learner): route to the largest model and verify
• Low variance, low stakes (internal classification): route to the smallest model, no verification
• Known-error domains (arithmetic, citation): prefer constrained decoding over sampling

This rule converts architecture choice from a quality debate into a statistical cost-minimization problem. The correct portfolio is the one whose expected cost per acceptable output is minimal — the principle made operational in Chapter 5's routing layer, and the reason SCHOOL expects a multi-model portfolio rather than a single-model dependency.

3.3 The Macro-Economics of the Educational Value Equation

3.3.1 The Equation and Its Implications

Chapter 3 closes with the value framework that governs the whole paper, applied to the educational case:

Educational Value per Investment = [Learning Outcomes × Reach × Retention × Expansion Potential] / [Direct Costs + Systemic Costs - Systemic Benefits + Regulatory Costs - Regulatory Benefits]

The numerator is the value the platform actually produces: outcome quality, student reach, retention, and the capacity to expand into adjacent offerings. The denominator is the true cost of production: direct token and compute spend, systemic costs (the overhead of the stack), minus systemic benefits (shared infrastructure recovered across products), plus regulatory costs, minus regulatory benefits (compliance that becomes a market advantage).

3.3.2 Architecture Choice as a Numerator Strategy

Architecture decisions act on both sides of the equation:

• On the numerator: the right model improves learning outcomes (better feedback) and reach (cheaper serving funds larger free-tier allocation)
• On the denominator: small models and sparse models compress direct costs; shared serving infrastructure across subjects spreads systemic costs
• On the balance: regulatory benefits are amplified when architecture choice enables provable standards alignment — a model small enough to audit, transparent enough to explain

The equation makes explicit what the paper has argued implicitly: model selection is not a procurement event but a value-leverage decision. A 7B–34B model that delivers 90% of frontier quality at 10% of the cost moves the equation more than any marketing line about model capability.

3.3.3 The Portfolio as the Unit of Analysis

The unit of economic analysis is not the model deployed today but the portfolio over the planning horizon:

• Served now: the routing decisions currently in production
• Re-served later: workloads that migrate between tiers as prices fall and models improve
• Retired: workloads absorbed into cheaper architectural families
• New: workloads that only become viable when some member of the portfolio crosses a cost threshold

Because token prices are exogenous and falling, the portfolio must be re-examined on a schedule, not an event. The venture that reviews its architecture mix quarterly captures price declines; the venture that reviews it on incident drift permanently lags the market.

Impact on Token Economics The taxonomy in this chapter converts an unbounded space of possible models into a small set of deployable tiers. With tiers defined, token economics becomes an accounting exercise: which tier serves which workload, at what expected cost per acceptable output. This is the bridge from architecture theory to the capital and operating budgets of Chapter 4, and to the serving-layer optimization of Chapter 5.

Chapter 3 Summary

Chapter 4: Training Costs and Capital Expenditure

The Real Price of Intelligence

4.1 Introduction to Training Economics

• Dense models have predictable, size-proportional economics
• Mixture-of-experts decouples capability from per-token cost
• Multimodal input is strategically necessary but structurally expensive
• Reliability, not raw capability, is what a venture actually buys
• Expected cost per acceptable output should govern architecture choice
• The educational value equation unifies cost and outcome in one ratio
• The portfolio, reviewed on a schedule, is the correct unit of analysis

Training is the moment an AI venture's economics are decided. At the frontier, a single training run costs between $10M and $100M+, a figure that — before any service is served — already exceeds the lifetime revenue of most software companies. The vast majority of ventures will never train a frontier model; they will instead fund, buy, or fine-tune models whose training costs were incurred elsewhere. Yet training economics governs everything downstream, because the price of every token sold on the market must, in equilibrium, recover the training and capital expenditures of the layer that produced it.

Chapter 4 therefore treats training in two registers:

4.2 The Anatomy of a Training Run

4.2.1 The Four Cost Pillars

• As a capital budget: what a training run actually spends money on, and how that spend is financed and depreciated
• As an operating fact: how training-derived costs flow into the token prices that application builders like SCHOOL pay

A training run is dominated by four pillars:

• Compute: accelerator-hours times device cost, the single largest line item
• Energy: power plus cooling over the duration of the run
• Data: curation, licensing, cleaning, and de-duplication of the training corpus
• Talent: the researchers, engineers, and operators who design and run the run

Frontier figures of $10M–$100M+ are almost entirely compute and energy. Data and talent are material at every scale but compress toward a minority share at frontier scale, where the denominator is dominated by silicon-hours.

4.2.2 Why Training Scales Super-Linearly

The cost of a training run grows with the product of parameters and data tokens, and both grow faster than capability:

• Parameters define memory, memory defines compute per step
• Tokens define steps, and protocol dictates that a model sees its data a limited number of times
• Validation and ablations multiply the effective cost: every schedule experiment is itself a training event

The consequence is that each doubling of capability associated with a doubling of scale carries a super-linear price tag. This is the fundamental reason frontier models are priced as capital goods and passed through to consumers as persistent token fees rather than one-time purchases.

4.2.3 The Energy Component and the Arbitrage Revisited

Recalling Chapter 2, energy is a first-class cost. A frontier run is a multi-week, uninterrupted power draw; the geographic arbitrage of 50–80% on the energy component is not a refinement but a pricing decision worth millions on a single run. The arbitrage loop from Chapter 2 has its most concentrated application here — before the model exists.

4.3 Training as Capital Expenditure

4.3.1 Capex vs. Opex

Every AI enterprise faces a capital structure choice:

• Own the run: buy hardware, pay energy, amortize over multiple uses
• Rent the run: buy compute-hours from a cloud, convert capital into operating expense
• Buy the result: license weights or API access, eliminating training capex entirely
• Finance the run: borrow against expected future token sales

Frontier laboratories mix these; application ventures overwhelmingly choose the third. The virtue of the choice is capital discipline: SCHOOL converts an impossible capex — a frontier training run — into a predictable per-token opex. The cost is strategic dependence on the layer that owns the weights.

4.3.2 Fine-Tuning as the Affordable Middle Path

Between buying frontier weights outright and self-serving general models lies fine-tuning: adapting an existing open-weight model to a narrow domain at a fraction of frontier cost:

• A fine-tuning run costs orders of magnitude less than pretraining because the base model has already paid the frontier capex
• Data becomes the differentiator: curriculum-aligned, misconception-tagged interaction data is scarce and valuable
• The result is specialized: a domain-tuned model routinely beats a general frontier model on the narrow task

For SCHOOL, the fine-tune path converts years of learner interaction data into a durable, capitalizable asset — a model that embodies the house's pedagogy. The data flywheel described in Chapter 6 depends on exactly this conversion.

4.3.3 Depreciation and the Falling Value of Weights

Trained weights depreciate. The token economics industry moves in quarters:

• New generations of base models arrive on an accelerating cadence
• An acquired model's advantage decays as newer, cheaper, or more capable alternatives arrive
• Fine-tunes must be re-adapted or re-built atop new bases

An asset whose value halves per year cannot be treated as a sunk cost; it must be re-deployed on a schedule. This is why the knowledge base of this paper emphasizes data efficiency — 2–5x reduction in required training tokens — as a strategic goal: the cheaper intelligence is to re-train, the shorter its depreciating half-life must be to remain viable.

4.4 The Linear-Attention Horizon and the End of the "Context Tax"

4.4.1 The Quadratic Context Tax

Standard self-attention computes pairwise interactions across all tokens, so cost grows quadratically with context length. Every long-document task — a student pasting a curriculum chapter, a tutor asked to reason over a term's notes — pays a context tax where the attention term, already the second-largest cost in Chapter 2, balloons without bound. The tax is not merely financial: it caps what applications can afford to put in context at all.

4.4.2 Linear Attention and Approximate Architectures

A generation of architectural work targets the quadratic term directly:

• Linear attention formulations that approximate pairwise attention in linear time
• Sparse and sliding-window patterns that cut off-distant interactions
• State-space and recurrent hybrids that compress history into a fixed-size state
• Retrieval-augmented selection that chooses which context to attend to, making the attended window a decision rather than a given

The direction of travel is unambiguous: the end of the context tax is an architectural near-term, not a research speculation. When long context becomes linear-cast, the Chapter 2 bookkeeping changes — weight streaming dominates even more decisively, and the economics of long-context products (legal, research, and educational reasoning over large corpora) reset downward.

4.4.3 What the Horizon Means for the Educational Platform

The linear-attention horizon is a price event for SCHOOL, not an engineering event:

• Long-context tutoring becomes affordable: grade-level curriculum corpora in context, reasoned over, at token prices a developing-market free tier can sustain
• The friction coefficient becomes cheaper to serve: deeper diagnostics against longer learner histories cost less per token
• The portfolio re-tunes: workload previously routed to frontier models for their long-context strength migrates to cheaper, linear-attention classes

The strategic discipline from Chapter 3 applies: do not let the architecture change arrive as a surprise. A venture on a fixed review schedule captures the price decline; a venture that reacts only to provider marketing pays yesterday's prices for today's capability.

Impact on Token Economics Training cost sets the equilibrium floor under every token price in the market. The venture that understands the anatomy of training — its pillars, its super-linear scaling, its capex structure — can predict price trends instead of reacting to them. Capital discipline (buy results, fine-tune what is differentiated, amortize on a schedule) plus early adoption of linear-attention economics converts a structural cost problem into a managed margin.

Chapter 4 Summary

Chapter 5: Inference Optimization

The Economics of the Token at Serve Time

5.1 Introduction to Inference Optimization

• Frontier training runs cost $10M–$100M+, dominated by compute and energy
• Training scales super-linearly with parameters and data tokens
• Energy arbitrage concentrates its value on the training run itself
• Capex structure — own, rent, buy, or finance — determines strategic freedom
• Fine-tuning converts proprietary data into a durable, specialized asset
• Weights depreciate on a market schedule and must be re-deployed accordingly
• The context tax is ending; linear-attention economics will reset long-context prices
• Application ventures should buy results, fine-tune what differentiates them, and schedule their price capture

Training determines what a model can do; inference determines what a venture can afford. The gap between advertised capability and served capability is bridged by inference engineering — the pursuit of the cheapest acceptable token. This is where the cost structure of an application venture is actually made, and it is the chapter with the most operational leverage in this paper.

The knowledge base establishes the stakes: software and algorithmic efficiency improvements deliver 2–10x gains before any hardware purchase, and data efficiency delivers 2–5x reduction in required training tokens. Inference optimization is the deployment face of those gains. This chapter examines:

5.2 The Serving Stack

5.2.1 Batching and the Amortized Stream

• The serving stack: batching, caching, and quantized weights at the hardware frontier
• The trust premium: why verification and reliability are assets with a measurable value
• Efficient attention: the algorithmic side of the Chapter 2 bottleneck
• The routing layer: matching workload to model tier; the operational core of this paper's thesis

Chapter 2 established that per-token weight streaming dominates cost. Batching is the correction: multiple users' tokens are generated concurrently, and the weight stream is shared across the batch. Effective throughput rises steeply until the batch saturates memory or arithmetic:

• Larger batches amortize the dominant cost across more users
• Continuous batching fills the batch in real time instead of waiting for a synchronous window
• The trade: latency per user rises modestly as the batch grows; parallel-server diversity mitigates tail effects

For a high-concurrency platform like SCHOOL, batching is the single most powerful lever in the serving stack. It converts a per-user weight-streaming tax into a shared, amortized cost without changing a single model parameter.

5.2.2 Caching and the Once-Paid Token

The KV-cache is the memory of the conversation: past context keyed into attention without re-computation. Caching strategies determine how often that memory is rebuilt:

• Priority caching preserves the tokens that future steps will actually attend to
• Context caching (prefix caching) reuses computation across users who share a prefix — the same curriculum preamble, the same system prompt
• Chunked eviction trades memory for recompute on a tuned schedule

The effect is that a token that was expensive to compute once is cheap to reuse many times. SCHOOL's shared curriculum contexts make it an unusually cache-friendly workload — an advantage that is simply captured, not engineered from nothing.

5.2.3 Quantization and the Shrunk Stream

Weights that are stored in fewer bits stream faster and occupy less memory:

• FP16 → INT8 halves the memory stream at minimal quality cost
• INT4 and mixed-scheme quantization trade further capacity for precision loss
• Calibration quality determines whether a quantized model's quality is indistinguishable from the unquantized original

Because per-token cost is proportional to streamed weight bytes, quantization is effectively a parameter-count reduction that requires no retraining. A 13B model quantized to INT8 behaves, in serving economics, like a 6.5B model with the capabilities of a 13B. The Chapter 3 taxonomy should therefore be read with quantization in mind: the deployable tier is defined by effective streamed bytes, not sticker parameters.

5.3 The Trust Premium as a Quantifiable Financial Asset

5.3.1 Verification as a Revenue Asset

Trust is usually discussed as a soft value; this chapter prices it. A trustworthy output — verified, grade-appropriate, curriculum-aligned — is worth more than the same output unverified, and the premium is quantifiable in the same units as token spend:

5.3.2 The Premium in the Value Equation

• Retention: learners who trust the tutor return; the knowledge base records 15–25% retention gains from network-effect quality
• Reduced CAC: trusted products convert with less persuasion; the knowledge base records 30–50% lower acquisition cost
• Regulatory benefit: demonstrable reliability converts compliance cost (5–15% of project budget) into a market advantage
• Premium pricing: verified tiers out-price unverified equivalents, widening the margin on served tokens

Recalling Chapter 3's value equation, the trust premium acts on both numerator and denominator simultaneously: it raises learning outcomes and retention (numerator) while converting regulatory costs into regulatory benefits (denominator). The same verification infrastructure that cuts retry waste is the evidence base that justifies institutional adoption.

5.3.3 The Balance Sheet Treatment

The trust premium is not a sentiment; it is an asset with an accounting treatment:

• Earned through measurement: every verified output, every retained learner is a transaction in the trust account
• Carried forward: trust compounds — early institutional adoption validates subsequent adoption
• Depreciable if neglected: a single widely-observed failure spends years of accrued trust

The discipline that emerges is that verification spend is not a cost; it is capital formation. SCHOOL's choice to route its highest-stakes interactions through the strongest models with human-in-the-loop validation is not a generosity line item — it is the balance-sheet management of the trust premium.

5.4 Efficient Attention and the Algorithmic Side

5.4.1 The Optimization Family

Building on the memory-bandwidth analysis of Chapter 2, attention optimization attacks the second term of per-token cost:

• FlashAttention and fused kernels keep attention working tiles resident in on-chip memory instead of round-tripping to HBM
• Sparse and sliding-window attention restrict which tokens attend to which, trading recall for cost
• Prefill decomposition treats the expensive context-encoding phase differently from decoding
• Speculative decoding has a small draft model propose tokens while the large model verifies several at once — turning one expensive step into one expensive verification plus several cheap propositions

These are not micro-optimizations; they compound into the 2–10x algorithmic-efficiency band of the knowledge base. The same model, the same hardware, and a materially different cost per token — achievable by software choices alone.

5.4.2 The Efficiency Stack, in Order of Capture

The rational order of optimization for a venture is:

1. Routing (Chapter 3 tiers): never serve a small task on a large model
2. Caching and batching: amortize what must be paid
3. Quantization: shrink the stream without retraining
4. Efficient attention: apply kernel and algorithmic advances as they mature
5. Distillation and fine-tuning: compress capability into smaller active models

The ordering matters because each step de-risks the next. Routing captures the largest, safest gains first; kernel adoption captures frontier-scale gains opportunistically; distillation locks capability into the cheapest served form. A venture executing this stack in order compounds gains rather than leaving them on the table.

5.5 The Routing Layer

5.5.1 The Whole-Portfolio Scheduler

The operational payoff of Chapter 3's taxonomy is the routing layer: a real-time decision about which model tier serves a given request. Routing inputs include:

• Task class: classification, generation, reasoning, perception, retrieval
• Stakes: the cost of a wrong answer in this context
• Friction coefficient (Chapter 1): the learner's uncertainty state
• Latency budget: how fast the user needs the answer
• Reliability target: the acceptable retry probability

A well-designed router serves 70–90% of routine workload on small or mid tiers and reserves frontier spend for the residual high-stakes, high-friction, perception-heavy requests. The economic effect is that the platform's average cost per token converges toward its cheapest acceptable tier rather than its most capable one.

5.5.2 Fallback, Retry, and the Verifier Loop

The router also owns failure:

• Confidence thresholds trigger escalation to a larger tier before a poor answer reaches a learner
• Verifier models check outputs for correctness and grade-appropriateness at token cost below the generator's
• Bounded retries cap the expected cost per interaction at a defined multiple of the first attempt

Chapter 3's variance-reliability pricing becomes operational here: the platform commits to an expected cost per acceptable output and enforces it with mechanisms, not hopes.

5.5.3 Data-Light Design as the South African Variable

SCHOOL's constraint is not purely financial; it is infrastructural. South African learners access the platform over mobile networks with metered data, and the knowledge base positions the platform as "data-light by design — built for South African connections." Inference optimization enters the product architecture itself:

• Streamed, incremental responses instead of bulky payloads
• Client-side caching of repeated content to avoid re-downloading tokens
• Tiered fidelity: low-bandwidth modes that trade generation length for reach
• Heavy processing on the server, light rendering on the device

The point is that optimization multiplies value at the margin that matters: the same token budget serves more learners when every served byte is engineered to be worth carrying. For SCHOOL the token is not abstract — it is a quantity of megabytes a student's data bundle must tolerate.

Impact on Token Economics Inference optimization compresses every domain of cost identified in earlier chapters: weight streaming (quantization, batching), attention (efficient kernels), selection (routing), and failure (verifier loops). The 2–10x algorithmic band and the 2–5x data band are not theoretical — they are the compounded result of the stack in this chapter. The trust premium then converts the platform's cheapest-possible-token discipline into durable revenue. Together these are the mechanism by which the token economics of this paper become the unit economics of an educational venture in Africa.

Chapter 5 Summary

Terminal Development History

Evolution of Human-Computer Interaction in the AI Era

1. Introduction to Terminal Evolution

The terminal—our primary interface for interacting with computational systems—has undergone a remarkable evolution that parallels and enables the development of artificial intelligence. From punched cards and teletypes to graphical terminals and modern IDEs, the evolution of terminal technology has shaped how humans communicate with machines, directly impacting the accessibility, usability, and adoption of AI systems.

Understanding this evolution is crucial for several reasons:

• Interaction Paradigms: Terminal innovations have shifted interaction paradigms from batch processing to real-time, conversational interfaces
• Accessibility: Terminal evolution has democratized access to computational resources
• Productivity: Improved interfaces have dramatically increased developer productivity in AI research and deployment
• AI Integration: Modern terminals increasingly incorporate AI-powered features like intelligent code completion and natural language command interpretation

2. Early Computing Interfaces (1940s-1960s)

2.1 Punched Cards and Batch Processing

• Mechanism: Programs and data encoded on physical cards
• Interaction Model: Submit job, wait hours/days for results
• Limitations: No interactivity, high latency, error-prone
• AI Relevance: Early AI research (Logic Theorist, 1956) used this model—severely limiting experimentation speed

2.2 Teletypes and Line Printers (1960s)

• Teletype Model 33/35: Electromechanical printers with keyboard input
• Communication: Serial connection (RS-232), typically 10-30 characters per second
• Interaction Model: Command-line interface with printed output
• Significance: First interactive computing systems (MIT's CTSS, 1961)
• AI Impact: Enabled early AI experimentation (ELIZA, 1966) but with severe limitations

2.3 Video Display Terminals (VDTs) (1970s)

• Examples: DEC VT52, VT100; IBM 3270
• Technology: Cathode-ray tube (CRT) displays replacing paper output
• Advancements:

- Real-time visual feedback

- Cursor positioning and screen addressing

- Standardized escape sequences (ANSI X3.64, VT100)

• Impact on AI: Enabled interactive debugging and iterative development—critical for early machine learning experiments

3. The Workstation Era (1980s-1990s)

3.1 Bitmapped Displays and Graphics

• Systems: Xerox Alto, Apple Lisa, Sun Workstations
• Innovations:

- Bitmapped graphics enabling arbitrary pixel control

- Windows, icons, menus, pointing (WIMP) interface

- High-resolution displays (640x480 and beyond)

• AI Relevance: Enabled visualization of complex data structures and neural network architectures

3.2 Networked Terminals and X Window System

• X11 (1984): Network-transparent window system
• Model: Client-server architecture for graphical displays
• Impact:

- Remote access to powerful graphics workstations

- Collaborative AI research environments

- Standardization of graphical interfaces across heterogeneous systems

3.3 Early Integrated Development Environments

• Examples: Smalltalk-80 environment, Turbo Pascal IDE
• Features:

- Integrated editing, compilation, debugging

- Syntax highlighting and code navigation

- Early forms of code completion

• AI Connection: Laid groundwork for modern AI-assisted development tools

4. The Terminal Renaissance (2000s-Present)

4.1 Linux Console and Terminal Emulators

• Linux VT: Virtual terminals on commodity hardware
• Emulators: xterm, GNOME Terminal, iTerm2, Windows Terminal
• Advancements:

- Unicode support for internationalization

- True color (24-bit) display

- GPU-accelerated rendering

- Ligatures and programming fonts (Fira Code, JetBrains Mono)

• AI Relevance: Enabled widespread access to AI development environments on affordable hardware

4.2 Rich Terminal Features

• Multiplexers: tmux, GNU screen (session persistence, window splitting)
• Enhanced Shells: zsh, fish (autosuggestion, syntax highlighting, robust scripting)
• Prompt Customization: Powerlevel10k, Starship (context-aware, git integration, execution timing)
• Output Enhancement: bat (cat with syntax highlighting), exa (ls replacement)
• Impact on AI Workflow: Dramatically improved productivity for ML engineers managing experiments, logs, and deployments

4.3 Integration with Development Ecosystems

• Language Servers: Language Server Protocol (LSP) enabling IDE-like features in terminals
• Debugging Integration: gdb, lldb with rich terminal interfaces
• Container Orchestration: Docker CLI, kubectl for managing AI workloads
• Monitoring Tools: htop, btop, glances, prometheus integrations
• AI-Specific Tools:

- Weights & Biases CLI for experiment tracking

- MLflow for model lifecycle management

- Hugging Face CLI for model hub interactions

5. AI-Augmented Terminals: The Next Frontier

5.1 Natural Language Interfaces

• Examples: GitHub Copilot CLI, Amazon CodeWhisperer CLI
• Functionality: Convert natural language to shell commands
• Impact: Lowering barrier to entry for complex system administration and DevOps tasks
• Economic Implications:

- Reduced training costs for infrastructure teams

- Faster prototyping and experimentation cycles

- Democratization of advanced system operations

5.2 Intelligent Command Completion and Correction

• Features:

- Context-aware argument completion

- Mistake prediction and correction (The Fuck, fasd)

- Command scoring based on frequency and recency

• AI Techniques:

- Sequence modeling (n-grams, transformers)

- Reinforcement learning from user corrections

- Embedding-based similarity for command suggestion

• Productivity Gains: 20-40% reduction in typing and error correction time

5.3 Real-Time Output Analysis and Filtering

• Capabilities:

- Automatic error detection and highlighting

- Smart pagination and filtering of verbose output

- Anomaly detection in log streams

- Performance bottleneck identification

• AI Applications:

- Monitoring training jobs for divergence or instability

- Detecting data pipeline issues in real-time

- Flagging security concerns in deployment logs

• Business Value: Reduced mean time to resolution (MTTR) for incidents

5.4 Collaborative and Shared Terminal Experiences

• Technologies:

- WebSocket-based terminal sharing (Gotty, ttyd)

- Collaborative editing in terminal multiplexers

- Shared session recording and playback

• Use Cases:

- Remote pair programming on AI infrastructure

- Training junior engineers on complex ML pipelines

- Auditorium-style demonstrations of AI workflows

- Asynchronous debugging assistance

• Economic Impact:

- Reduced onboarding time for specialized AI roles

- Improved knowledge transfer in distributed teams

- Enhanced support efficiency for complex AI systems

6. Terminal Evolution and AI Accessibility

6.1 Lowering Barriers to Entry

• Historical Trend: Each terminal advancement has broadened access to computational power
• Current Impact: Modern terminals with AI assistance enable:

- Students and hobbyists to experiment with LLM fine-tuning

- Small startups to manage complex AI infrastructure

- Researchers in resource-constrained environments to participate in AI advancement

• Economic Effect: Democratization of AI development through improved accessibility

6.2 Changing Skill Requirements

• Shift: From memorizing arcane commands to conceptual understanding supplemented by AI assistance
• New Competencies:

- Effective prompting of AI-assisted terminals

- Interpretation of AI-generated suggestions

- Critical evaluation of automated recommendations

- Hybrid human-AI workflow design

• Training Implications:

- Reduced emphasis on rote memorization

- Increased focus on problem-solving and conceptual understanding

- Need for AI literacy in technical education

6.3 Accessibility Enhancements

• Features:

- Screen reader compatibility improved through semantic output

- Alternative input methods (voice-to-command via AI)

- Customizable visual themes for neurodiversity

- Language localization through real-time translation

• Impact: Expanded participation in AI development across diverse populations
• Social Value: More inclusive AI development leading to fairer and less biased systems

7. The Terminal as an AI Interaction Paradigm

7.1 Beyond Command Lines: Conversational Development

• Emerging Patterns:

- Iterative refinement through dialogue ("make this faster", "explain this error")

- Context-aware assistance based on project structure and history

- Proactive suggestions based on observed workflows

- Multi-modal interaction (code, diagrams, natural language)

• Examples:

- Cursor AI editor with integrated terminal

- GitHub Copilot Chat in terminals

- JetBrains AI Assistant in IDE terminals

• Paradigm Shift: From tool-as-medium to tool-as-collaborator

7.2 Implications for AI Economics

• Development Velocity:

- Faster prototyping reduces time-to-experiment

- Lower error rates decrease debugging overhead

- Accelerated onboarding increases team scalability

• Cost Structure Changes:

- Reduced senior engineer dependency for routine tasks

- Shift from execution to supervision and guidance

- New value in prompt engineering and AI interaction design

• Innovation Acceleration:

- More experiments per researcher per unit time

- Lower cost of failure encourages exploration

- Rapid iteration on model architectures and training strategies

8. Future Trajectories in Terminal Technology

8.1 Immersive and Spatial Interfaces

• Augmented Reality Terminals:

- holographic command prompts in physical workspace

- spatial arrangement of logs and outputs

- gesture-based command modification

• Virtual Reality Development Environments:

- immersive data visualization alongside terminal workflows

- collaborative virtual spaces for AI architecture design

• Potential Impact:

- Enhanced spatial reasoning for complex system architectures

- Reduced context switching between tools and modalities

- New interaction paradigms for distributed AI systems

8.2 Predictive and Proactive Assistance

• Anticipatory Computing:

- Pre-loading likely needed tools and dependencies

- Predictive environment configuration based on task context

- Proactive suggestions for optimization and best practices

• Workflow Automation:

- End-to-end automation of common AI development patterns

- Self-healing development environments

- Autonomous routine maintenance and updates

• Economic Effect:

- Further reduction in cognitive overhead

- Increased focus on novel problem-solving

- Shift toward higher-value creative tasks

8.3 Integration with Broader AI Ecosystems

• Unified Context Awareness:

- Terminal awareness of IDE state, design documents, issue trackers

- Cross-tool suggestion propagation (e.g., from architecture diagram to implementation)

- Holistic project understanding for more relevant assistance

• Feedback Loops:

- User corrections improving underlying models

- Outcome-based refinement of assistance strategies

- Community-driven model specialization for specific domains

• Long-Term Vision:

- The terminal as a node in an ambient AI assistance ecosystem

- Seamless transition between conversation, coding, and system operation

- Continuous learning from individual and organizational patterns

9. Case Study: Terminal Evolution in AI Research Workflows

9.1 Historical Comparison: LeNet-5 (1998) vs. Modern LLM Training

1998 LeNet-5 Development (Yann LeCun et al.):

• Environment: Sun Sparc workstations with CDE desktop
• Primary Interface: xterm terminals with emacs/vi
• Workflow:

- Edit code in terminal-based editor

- Compile with make (manual dependency tracking)

- Run experiments, capture output to files

- Analyze results with gnuplot or custom scripts

- Debug with gdb in terminal

• Cycle Time: Hours to days per experiment iteration
• Collaboration: Email patches, scheduled meetings

2024 LLM Fine-tuning Workflow:

• Environment: Linux containers or cloud VMs
• Interface: Enhanced terminal (WezTerm) with tmux, starship, fzf
• AI Assistance:

- GitHub Copilot for code suggestions

- Auto-complete for hyperparameters and paths

- Error explanation and fixing

• Workflow:

- Natural language request: "help me set up LoRA fine-tuning for Llama 3"

- AI generates baseline command with explanations

- Interactive refinement through dialogue

- Real-time tensorboard-like metrics in terminal via custom scripts

- Automatic checkpointing and recovery suggestions

- Collaborative debugging via shared tmux sessions

• Cycle Time: Minutes to hours per experiment iteration
• Collaboration: Live shared sessions, asynchronous video reviews

Productivity Impact:

• Estimated 5-10x reduction in time per experiment cycle
• 3-5x increase in experiments per researcher per week
• Significant reduction in cognitive load for routine tasks

9.2 Economic Analysis of Terminal Advancements

Time Savings Quantification:

• Command Lookup and Typing: 30-50% reduction with intelligent completion
• **Error Diagnosis': 40-60% reduction with AI-assisted error explanation
• Environment Setup: 50-70% reduction with automated configuration
• Collaboration Efficiency: 25-40% improvement with shared terminal sessions
• Learning Curve: 60-80% reduction in onboarding time for complex systems

Cost Implications for AI Organizations:

• Direct Savings: Reduced engineer hours for equivalent output
• Opportunity Cost Reduction: More time for innovation vs. maintenance
• Quality Improvement: Fewer errors leading to more reliable systems
• Scalability Enhancement: Ability to manage more complex systems with same team size

10. Conclusion: The Terminal as an AI Force Multiplier

The evolution of terminal technology from punched cards to AI-augmented command interfaces represents a critical but often overlooked enabler of AI advancement. Far from being a passive conduit, the terminal has actively shaped how humans conceive, implement, and interact with artificial intelligence systems.

Key insights for stakeholders:

1. Interaction Drives Adoption: The usability and power of interfaces directly impact who can participate in AI development and how quickly they can contribute

2. Productivity Multipliers: Modern terminal enhancements provide compounding benefits—each small efficiency gain multiplies across thousands of daily interactions

3. Democratization Effect: Each leap in terminal accessibility has broadened participation in computational fields, a trend continuing with AI-assisted interfaces

4. Future Integration: The boundary between terminal, IDE, and AI assistant is blurring, creating more intuitive and powerful development environments

5. Strategic Investment: Organizations that invest in advanced terminal tooling and AI-augmented workflows gain measurable advantages in innovation speed and talent retention

For entities like SCHOOL (Pty) Ltd, investing in modern terminal infrastructure and AI-enhanced interfaces represents a high-leverage strategy:

• Immediate Benefits: Increased productivity for AI development teams
• Medium-Term Advantages: Faster iteration on educational AI models and tools
• Long-Term Positioning: Attraction and retention of top talent seeking cutting-edge development environments
• Educational Value: Exposure to professional-grade tools prepares students for real-world AI development

As we continue to push the boundaries of what AI can accomplish, the evolution of our interface with these systems remains a critical factor in determining not just what we build, but who gets to build it and how quickly we can advance. The humble terminal, far from obsolete, stands at the forefront of this ongoing revolution in human-AI collaboration.

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Chapter 6: Ecosystem Effects

Network Effects, Platform Dynamics, and Systemic Value in AI Economics

6.1 Introduction to Ecosystem Effects in AI

While chapters 4 and 5 examined the direct costs of training and inference, this chapter explores the broader ecosystem effects that profoundly influence the economics of AI systems. These network effects, platform dynamics, and complementary innovations create value (or costs) that extend far beyond the immediate production and consumption of AI services.

Ecosystem effects in AI manifest through multiple channels:

• Direct network effects: Where the value of a model or service increases with the number of users
• Indirect network effects: Where complementary products, tools, or services enhance the core offering
• Data network effects: Where user interactions generate valuable training data
• Platform ecosystems: Where developer communities build extensions, tools, and applications
• Standardization effects: Where common frameworks and interfaces reduce friction
• Talent and knowledge spillovers: Where expertise diffuses through communities and organizations

Understanding these effects is crucial because they can dramatically alter the effective cost structure of AI systems. A model that appears expensive in isolation may become highly economical when ecosystem benefits are considered, while seemingly cheap alternatives may carry hidden ecosystem costs.

This chapter examines these dynamics through the lens of token economics and compute costs, using SCHOOL (Pty) Ltd as a case study to illustrate how educational technology providers can leverage ecosystem effects to improve the viability of their AI offerings.

6.2 Direct Network Effects in AI Services

Direct network effects occur when the value of a service increases directly with the number of users. In AI, these effects are often subtle but significant:

6.2.1 User Base Effects on Model Quality

For many AI applications, particularly those involving recommendation systems, language understanding, or generative tasks, more users generate valuable interaction data that can improve model performance:

Implicit Feedback Loops:

• User corrections, refinements, and follow-up queries provide implicit training signals
• Usage patterns reveal which outputs are most valuable or actionable
• Error patterns highlight model weaknesses requiring attention
• Example: A math tutoring system improves as it sees more student attempts and corrections

Explicit Feedback Mechanisms:

• Thumbs up/down ratings, correction interfaces, and preference indicators
• Structured feedback forms for specific use cases
• Expert review processes for high-stakes outputs
• Example: SCHOOL's essay feedback system improves as teachers validate or correct AI-generated comments

Impact on Token Economics:

• Improved model quality reduces the need for expensive retry loops or human intervention
• Higher-quality outputs mean fewer tokens wasted on irrelevant or incorrect generations
• Better user satisfaction increases retention and lifetime value
• Network effects can effectively reduce the cost per useful token over time

6.2.2 Critical Mass and Viability Thresholds

Many AI services exhibit threshold effects where value increases non-linearly after reaching certain user scales:

Minimum Viable Population (MVP):

• Some features (e.g., peer explanation generation, collaborative problem-solving) require critical mass
• Below threshold: Feature is unusable or poor quality
• Above threshold: Value increases rapidly with additional users
• Example: SCHOOL's peer-learning features require sufficient concurrent users in each subject/grade level

**Network effects and Platform Dynamics in AI Economics

6.3 Indirect Network Effects and Complementary Ecosystems

Indirect network effects occur when the value of a core product increases due to complementary products or services. In AI, these ecosystems are particularly rich and valuable:

6.3.1 Tooling and Integration Ecosystems

The value of an AI model increases significantly with the availability of tools that make it easier to use, customize, and integrate:

Development Tools:

• SDKs and APIs in multiple languages (Python, JavaScript, Java, etc.)
• Low-code/no-code interfaces for non-technical users
• Integration platforms (Zapier, Make.com, custom connectors)
• Example: SCHOOL provides REST APIs, Python SDK, and Moodle/LMS plugins for easy integration

Customization and Fine-tuning Tools:

• Parameter tuning interfaces for non-experts
• Visual workflow builders for complex AI pipelines
• Template systems for common educational use cases
• Example: SCHOOL's "AI Activity Builder" lets teachers create custom learning experiences without coding

Monitoring and Observability Tools:

• Usage analytics and performance dashboards
• Bias and fairness monitoring tools
• Cost tracking and optimization recommendations
• Example: SCHOOL's educator dashboard shows token usage per class, subject, and learning objective

Impact on Token Economics:

• Reduced integration costs lower the effective price of adoption
• Better monitoring reduces wasteful usage and optimization opportunities
• Customization tools increase perceived value, allowing for premium pricing
• Ecosystem tools can shift costs from the provider to users who value specific functionalities

6.3.2 Model Adaptation and Fine-tuning Communities

Communities that create adaptations, fine-tunes, and specialized versions of base models create significant value:

Prompt Engineering Communities:

• Shared prompt libraries for common educational tasks
• Best practices for specific age groups, subjects, or learning objectives
• Prompt chaining and workflow templates
• Example: SCHOOL maintains a community prompt library for math problem generation, essay feedback, and science explanations

Fine-tuning and Adapter Ecosystems:

• Community-shared LoRA adapters for specialized subjects or teaching styles
• Domain-specific fine-tunes (e.g., for special education, AP courses, language learning)
• Techniques for adapting models to local curricula and standards
• Example: SCHOOL teachers share adapters tuned to specific state standards or textbook series

Evaluation and Benchmarking Communities:

• Shared test suites for educational effectiveness
• Benchmarking frameworks for comparing approaches
• Best practices for measuring learning outcomes
• Example: SCHOOL contributes to and uses the "EdEval" benchmark suite for educational AI models

Impact on Token Economics:

• Community adaptations reduce the need for expensive custom model training
• Specialized variants often provide better performance per token for specific use cases
• Shared evaluation reduces wasted experimentation on ineffective approaches
• Network effects accelerate innovation cycles beyond what a single organization could achieve

6.4 Data Network Effects and the Data Flywheel

Perhaps the most powerful ecosystem effect in AI comes from data network effects, where user interactions generate data that improves the service, attracting more users who generate more data:

6.4.1 The AI Data Flywheel

The classic data flywheel operates as follows:

1. More users → More interaction data

2. More interaction data → Better model performance (via fine-tuning, RLHF, etc.)

3. Better model performance → Improved user satisfaction and retention

4. Improved user satisfaction → More users (returning to step 1)

Key Components for Educational AI:

• Interaction Logs: Student responses, time-on-task, help-seeking behaviors
• Feedback Signals: Teacher corrections, student ratings, outcome measurements
• Curriculum Alignment Data: Which outputs align with learning objectives and standards
• Misconception Patterns: Common errors and misunderstandings that reveal teaching opportunities

Implementation Considerations:

• Privacy-preserving techniques (differential privacy, federated learning)
• Clear consent mechanisms and transparent data usage policies
• Ethical guidelines for educational data use
• Balancing personalization with equity concerns

Impact on Token Economics:

• Improved model efficiency means fewer tokens needed for equivalent quality
• Reduced hallucinations and errors decrease wasted generation
• Better personalization increases engagement and learning efficiency
• The flywheel can create sustainable advantages that are difficult for competitors to replicate

6.4.2 Data Pooling and Collaborative Learning

Beyond individual platform effects, broader data sharing arrangements can create industry-wide benefits:

Consortium Data Sharing:

• Educational institutions pooling anonymized interaction data
• Subject-specific consortia (math educators, language teachers, etc.)
• Grade-band collaboratives (elementary, middle, high school specialists)
• Example: SCHOOL participates in the "Global Education AI Consortium" sharing de-identified interaction patterns

Pre-competitive Research Collaborations:

• Jointly funded research on effective educational AI techniques
• Shared benchmarks and evaluation methodologies
• Open publication of non-proprietary findings
• Example: Multiple edtech companies collaborating on research into effective math feedback strategies

Regulatory and Standards-Driven Sharing:

• Government-mandated sharing for educational equity initiatives
• Standards bodies creating common data formats and exchange protocols
• Public-private partnerships for educational innovation
• Example: National education departments creating approved datasets for AI training

Impact on Token Economics:

• Access to broader datasets improves model generalization and reduces overfitting
• Shared research reduces duplicate effort and accelerates progress
• Standardized interfaces reduce integration costs across the ecosystem
• Collective action can address challenges too large for any single entity

6.5 Platform Ecosystems and Marketplace Dynamics

Many successful AI strategies involve creating platforms that enable third-party value creation, similar to app stores or operating systems:

6.5.1 AI-as-a-Platform Models

Rather than selling end-user applications directly, some organizations provide platforms where others can build AI-powered educational tools:

Platform Components:

• Foundational models and APIs fine-tuned for educational use cases
• Developer tools, SDKs, and documentation
• Sandbox environments for testing and experimentation
• Monetization and billing infrastructure
• Example: SCHOOL launches "SchooLab" - a platform for educators to build and monetize AI teaching assistants

Value Creation Mechanisms:

• Network effects between developers and educators
• Reduced barriers to entry for educational innovators
• Diversification of use cases beyond what the platform owner could conceive
• Revenue sharing models that align incentives
• Example: A special education teacher creates a specialized assistant for dyslexic learners using SchooLab, earning revenue while expanding the platform's value

Impact on Token Economics:

• Platform fees create revenue streams beyond direct token sales
• Ecosystem innovation discovers high-value use cases that improve overall token efficiency
• Risk distribution across multiple creators rather than centralized R&D burden
• Platform data improves core models through aggregated, anonymized usage patterns

6.5.2 Marketplace Dynamics and Curation

Successful platforms require thoughtful curation and governance to maintain quality and trust:

Quality Control Mechanisms:

• Review processes for educational appropriateness and accuracy
• Bias and fairness audits for submitted models/tools
• Performance benchmarks and efficiency standards
• Example: SchooLab requires all submissions to pass educational validity checks before publication

Discoverability and Recommendation Systems:

• Search and categorization for educational resources
• Personalized recommendations based on teaching subject, grade level, and pedagogy
• Trending and popularity metrics within educational communities
• Example: Teachers discover popular math fact fluency tools through subject-specific browsing

Monetization and Revenue Sharing:

• Transparent revenue share models (e.g., 70/30 split favoring creator)
• Tiered pricing based on usage and premium features
• Educational discounts and institutional licensing options
• Example: SchooLab takes 30% of revenue, with volume discounts for high-earning creators

Impact on Token Economics:

• Marketplace competition drives efficiency innovations that benefit the entire ecosystem
• Successful applications reveal patterns that can improve core platform offerings
• Clear pricing structures help educators predict and manage token-related costs
• Innovation accelerates as creators build on each other's work

6.6 Standards, Interoperability, and Compatibility Effects

Ecosystem value is significantly enhanced when components can work together seamlessly through shared standards:

6.6.1 Technical Standards for Educational AI

Common interfaces and data formats reduce friction and increase combinatorial innovation:

Model Interchange Formats:

• Standardized formats for sharing models, adapters, and fine-tunes
• Metadata standards for educational alignment, age appropriateness, and subject matter
• Versioning and provenance tracking for educational auditing
• Example: The "EDU-MODEL" format specifies how to tag models with curriculum standards

API and Interface Standards:

• Common endpoints for educational AI functions (explain, assess, generate, adapt)
• Consistent parameter schemas for temperature, length, creativity controls
• Standardized error codes and response formats
• Example: All SCHOOL APIs follow the "EdAI v1.0" specification for educational consistency

Data Exchange Formats:

• Standardized schemas for student work, responses, and feedback
• Interoperable formats for learning analytics and progress tracking
• Privacy-preserving data sharing protocols
• Example: The "EdExchange" format allows seamless transfer of student-AI interaction data between systems

Impact on Token Economics:

• Reduced integration costs when switching between or combining services
• Increased competition drives down prices and improves quality
• Easier composition of complex educational workflows from simpler components
• Network effects as more tools become compatible with each other

6.6.2 Credentialing and Trust Frameworks

In educational contexts, trust and validation mechanisms are essential for adoption:

Quality Assurance Programs:

• Certification processes for educational AI tools
• Alignment reviews with state and national standards
• Accessibility and inclusivity evaluations
• Example: SCHOOL's AI tools carry the "EdTrust" certification indicating pedagogical soundness

Transparency and Explainability Standards:

• Requirements for showing work or reasoning steps in educational contexts
• Audit trails for compliance and improvement purposes
• Explainability interfaces appropriate for different age groups
• Example: Math problem solvers must show step-by-step reasoning to receive educational certification

Privacy and Safety Frameworks:

• Compliance with FERPA, COPPA, GDPR-K, and other educational privacy regulations
• Content filtering and safety monitoring systems
• Parental oversight and controls mechanisms
• Example: All student interactions are logged for review while maintaining privacy protections

Impact on Token Economics:

• Trust reduces sales friction and accelerates adoption
• Standardized compliance reduces legal and regulatory overhead
• Clear quality signals help educators make efficient purchasing decisions
• Safety features prevent costly incidents and reputational damage

6.7 Talent Ecosystems and Knowledge Spillovers

The availability of skilled practitioners and shared knowledge significantly affects the economics of AI deployment:

6.7.1 Educational AI Talent Pools

Concentrations of expertise create advantages for organizations located in or able to access these ecosystems:

Specialized Skill Development:

• Programs combining ML expertise with pedagogical knowledge
• Certifications for educational AI implementation and evaluation
• Communities of practice sharing classroom-tested techniques
• Example: Universities offering "AI in Education" specializations within computer science and education departments

Knowledge Sharing Mechanisms:

• Conferences focused on AI in education (AIED, ITS conferences)
• Professional learning communities for educators experimenting with AI
• Open educational resources (OER) for teaching about and with AI
• Example: Annual "AIEd Summit" where educators and researchers share effective practices

Recruitment and Retention Advantages:

• Easier hiring when local talent understands both AI and education
• Reduced onboarding time for domain-specific knowledge
• Professional development opportunities that retain talent
• Example: SCHOOL partners with local universities to create internship pipelines for educational AI talent

Impact on Token Economics:

• Skilled teams implement optimizations more quickly and effectively
• Reduced trial-and-error in prompt engineering and model tuning
• Faster adaptation to new techniques and architectures
• Lower costs associated with expertise acquisition and retention

6.7.2 Open Source Contributions and Community Labor

Open source ecosystems provide valuable resources that reduce development costs:

Framework and Library Contributions:

• Educational-specific extensions to popular ML frameworks
• Tools for evaluating educational effectiveness of AI systems
• Utilities for working with educational data formats and standards
• Example: The "EduTorch" extension provides educational data loaders and evaluation metrics

Pre-built Components and Templates:

• Prompt templates for common educational tasks
• Fine-tuning recipes for subject-specific adaptation
• Architecture templates for educational AI systems
• Example: Community-contributed transformer architectures optimized for explaining mathematical concepts

Bug Fixes and Security Improvements:

• Community-identified issues in educational AI applications
• Privacy and safety enhancements from diverse perspectives
• Accessibility improvements for diverse learner needs
• Example: Community contribution identifies and fixes bias in math problem generation for certain demographics

Impact on Token Economics:

• Reduced development costs for educational AI features
• Faster implementation of best practices from the community
• Shared security and compliance burden
• Access to innovations that would be expensive to develop in-house

6.8 Case Study: Ecosystem Effects at SCHOOL (Pty) Ltd

Applying these concepts to our primary case study reveals how SCHOOL can leverage ecosystem effects to improve the economics of its AI offerings:

6.8.1 Current Ecosystem Position

SCHOOL has begun developing several ecosystem components:

• Core AI models fine-tuned for educational use cases
• Basic API and SDK for integration with learning management systems
• Educator dashboard for monitoring usage and effectiveness
• Initial community forum for sharing teaching ideas

Opportunities for Enhancement:

1. Develop a comprehensive educational AI platform (SchooLab concept)

2. Establish data sharing consortia with other educational institutions

3. Create formal certification programs for AI-assisted teaching

4. Build extensive template and prompt libraries for common educational scenarios

5. Develop specialized evaluation frameworks for educational AI effectiveness

6.8.2 Quantifying Ecosystem Benefits

While ecosystem effects can be difficult to quantify precisely, several approaches can estimate their impact:

Cost Reduction Estimates:

• Integration cost reduction: 30-50% from standardized APIs and pre-built connectors
• Development acceleration: 40-60% from community components and templates
• Support cost reduction: 25-40% from self-serve documentation and community help
• Training cost reduction: 35-50% from standardized educational materials and certifications

Value Creation Estimates:

• Increased user retention: 15-25% from ecosystem lock-in and complementary value
• Premium pricing potential: 10-20% for ecosystem-enhanced offerings
• Expansion revenue: 20-40% from marketplace and platform services
• Viral acquisition reduction: 30-50% from community-driven growth

Risk Mitigation Benefits:

• Compliance cost reduction: 20-35% from shared best practices and tools
• Innovation risk reduction: 30-50% from distributed R&D across ecosystem
• Reputation risk reduction: 15-30% from community vetting and standards adherence

6.8.3 Strategic Recommendations for Ecosystem Development

Based on the analysis, SCHOOL should consider the following ecosystem development priorities:

Short-term (0-6 months):

1. Publish comprehensive API documentation with SDKs for major languages (Python, JavaScript)

2. Launch educator community forum for sharing prompts, tips, and best practices

3. Create template library for common educational use cases (lesson planning, quiz generation, feedback)

4. Establish basic data sharing agreements with pilot schools for improvement data

Medium-term (6-18 months):

1. Develop SchooLab platform for third-party educational AI tool creation and distribution

2. Launch formal certification program for "AI-Enhanced Educator" credentials

3. Join or create educational AI consortium for pre-competitive research and data sharing

4. Implement standardized educational metadata for all models and tools

Long-term (18-36 months):

1. Establish educational AI standards body participation or leadership

2. Create venture or grant program for innovative educational AI startups

3. Develop interoperability framework with major LMS and SIS platforms

4. Launch annual educational AI conference or significant presence at existing events

6.9 Ecosystem Effects and Token Economics: Synthesis

Understanding ecosystem effects transforms how we think about token economics in AI:

Beyond Simple Cost-per-Token Models:

Traditional token economics focuses narrowly on:

```

Cost per token = (Infrastructure cost + Energy cost + Overhead) / Tokens produced

```

Ecosystem-Enhanced Token Economics:

A more comprehensive model includes ecosystem factors:

```

Effective cost per educational outcome =

[(Infrastructure cost + Energy cost + Overhead)

• Ecosystem cost savings

+ Ecosystem development investment]

/ (Tokens produced × Ecosystem quality multiplier)

```

Where:

• Ecosystem cost savings include reduced integration, development, support, and training costs
• Ecosystem development investment includes platform development, community management, and standards participation
• Ecosystem quality multiplier represents improvements in relevance, accuracy, and pedagogical effectiveness from ecosystem inputs

Strategic Implications:

1. Investing in ecosystem can reduce effective costs even if it increases direct spending

2. Different user segments experience different ecosystem benefits (e.g., novice vs. expert educators)

3. Platform strategies may be optimal for organizations with strong community engagement potential

4. Open approaches often create larger total value, even if capturing a smaller percentage

5. Educational context amplifies certain ecosystem effects (trust, standards, pedagogical validity)

6.10 Conclusion

Ecosystem effects represent a powerful but often underappreciated dimension of AI economics. For organizations like SCHOOL (Pty) Ltd operating in the educational technology space, deliberately cultivating ecosystem advantages can transform the economics of AI from a challenging cost center into a sustainable source of educational value and innovation.

Key takeawys for stakeholders:

1. Look beyond direct costs: Ecosystem effects can significantly alter the effective economics of AI systems

2. Invest strategically: Not all ecosystem investments yield equal returns; focus on high-leverage opportunities

3. Think platformically: Consider how to enable others to create value with your AI foundations

4. Value openness appropriately: Open ecosystems often create larger total value, even with different capture mechanisms

5. Measure holistically: Develop metrics that capture both direct costs and ecosystem benefits/value creation

6. Prioritize educational alignment: In educational contexts, ecosystem effectiveness must serve pedagogical goals

7. Plan for evolution: Ecosystem strategies should evolve as the technology and educational landscapes change

By recognizing and actively shaping ecosystem effects, organizations can create AI offerings that are not only economically viable but also educationally transformative—delivering superior learning outcomes while managing the inherent costs of advanced AI systems.

---

Chapter 7: Advanced Architectures

Emerging Models and Their Economic Implications

7.1 Introduction to Advanced Architectures

While Chapters 1-3 covered established transformer-based architectures and their variants, this chapter looks toward the frontier of AI model design. Advanced architectures represent the next wave of innovation that promises to reshape the economics of AI systems by addressing fundamental limitations in current approaches.

As we've seen throughout this paper, the economics of AI are deeply intertwined with architectural choices. Training costs, inference efficiency, token consumption, and scalability all derive from how models are structured. Advanced architectures aim to break existing trade-offs—offering better performance with less compute, lower token requirements for equivalent quality, or entirely new capabilities that create economic value.

This chapter examines several promising architectural directions, analyzing their technical merits and, critically, their economic implications. For each approach, we'll consider:

• Technical innovations and how they differ from current paradigms
• Training economics: How do they affect data and compute requirements?
• Inference economics: What are the implications for token consumption, latency, and cost?
• Scaling properties: Do they follow different scaling laws than transformers?
• Ecosystem effects: How do they impact tooling, community development, and adoption?
• Strategic considerations: When and how might organizations like SCHOOL (Pty) Ltd leverage these advances?

Understanding these advances is essential for making informed decisions about long-term AI strategy, infrastructure investment, and model development priorities.

7.2 State Space Models and Linear Attention Alternatives

One of the most active areas of architectural innovation seeks to replace or augment the quadratic self-attention mechanism that has been both the strength and limitation of transformer architectures.

7.2.1 Mamba: Selective State Spaces (Mamba)

Technical Innovation:

Mamba replaces the attention mechanism with selective state spaces, achieving linear complexity O(n) for sequence length n instead of the quadratic O(n²) of standard attention. Key innovations include:

• Selection mechanism: Unlike earlier state space models, Mamba dynamically selects which information to propagate through the state based on the input
• Hardware-aware design: Optimized for efficient implementation on modern accelerators
• Structured state spaces: Uses structured (e.g., diagonal plus low-rank) state matrices for computational efficiency
• Parallel scan algorithm: Enables parallel training despite the recurrent formulation

Economic Implications:

• Training Costs: Linear complexity reduces training compute for long sequences

- For context length L, attention is O(L²) while Mamba is O(L)

- Example: Going from 2K to 32K context window increases attention cost 256x but Mamba cost only 16x

- Training large-context models becomes dramatically more economical

• Inference Efficiency:

- Constant latency per token regardless of context length (vs. linear increase for attention)

- Reduced memory bandwidth requirements for KV cache storage

- Particularly beneficial for applications requiring long context (document understanding, code generation)

• Memory Requirements:

- Significantly reduced KV cache storage needs

- Lower activation memory during training and inference

- Enables larger batch sizes or longer contexts on fixed hardware

• Scaling Laws:

- Preliminary evidence suggests different (potentially more favorable) scaling exponents

- May achieve similar performance with less compute for certain tasks

- Architecture appears to benefit from scale similarly to transformers but with better constants

Applications and Token Economics:

• Long-context understanding: Processing entire textbooks, codebases, or conversation histories

- SCHOOL application: Analyzing full semester of student work for personalized recommendations

- Token efficiency: Same quality with fewer tokens due to better long-range coherence

• Real-time applications: Streaming audio/video processing, live tutoring sessions

- Consistent latency enables predictable user experience

- Reduced token waste from context truncation or chunking

• Edge deployment: More feasible on resource-constrained devices

- Enables offline educational applications with sophisticated capabilities

Adoption Considerations:

• Maturity: Emerging but showing strong results on language modeling benchmarks
• Ecosystem: Growing support in Hugging Face Transformers, custom kernels available
• Compatibility: Drop-in replacement for attention in many architectures
• Implementation: Requires specialized kernels but libraries are maturing rapidly

7.2.2 Retentive Networks (RetNet)

Technical Innovation:

RetNet replaces attention with a retention mechanism that enables both parallel training (like transformers) and recurrent inference (like RNNs), getting the best of both worlds:

• Dual formulation: Training uses parallelizable formulation; inference uses efficient recurrent form
• Linear complexity: O(n) for both training and inference with respect to sequence length
• Simple implementation: Remarkably close to standard RNN implementation but with transformer-like performance
• Position encoding: Built-in effective position encoding without separate embeddings

Economic Implications:

• Training Efficiency:

- Parallel training maintains high hardware utilization

- Reduced memory footprint compared to attention mechanisms

- Faster convergence reported in early studies

• Inference Efficiency:

- True constant latency per token (no context length dependence)

- Minimal memory footprint for state storage (fixed size regardless of context)

- Enables streaming applications with bounded memory

• Memory Requirements:

- Fixed-size state instead of growing KV cache

- Dramatically reduced memory for long-generation tasks

- Particularly beneficial for autoregressive generation use cases

• Scaling Properties:

- Appears to follow standard transformer scaling laws with improved constants

- May enable larger effective models within fixed compute budgets

Applications and Token Economics:

• Generation-heavy workloads: Story writing, code generation, explanation creation

- SCHOOL application: Generating detailed lesson plans or multi-step problem solutions

- Token efficiency: More coherent long-form outputs with less token waste

• Interactive applications: Real-time tutoring, conversational agents

- Consistent low latency improves user experience

- Enables always-on educational assistants with minimal resource footprint

• Resource-constrained environments: Offline or low-power educational tools

- Enables sophisticated AI on older hardware or mobile devices

Adoption Considerations:

• Simplicity: Extremely simple to implement and understand
• Performance: Competitive with transformers on language modeling tasks
• Library support: Increasing availability in major frameworks
• Transition path: Straightforward to experiment with in existing codebases

7.2.3 Linear Attention and Kernel-Based Approaches

Several approaches approximate attention using kernel methods to achieve linear complexity:

Performer (FAVOR+):

• Uses positive orthogonal random features to approximate attention
• Provides unbiased estimation with controllable variance
• Linear complexity O(n) with small constant factors
• Economic impact: Reduced compute for long sequences, though accuracy can lag behind full attention

Linformer:

• Projects keys and values to lower-dimensional space
• Complexity O(n) where n is sequence length
• Economic impact: Significant memory and compute savings for long sequences
• Limitation: Performance degrades for tasks requiring precise positional reasoning

Nyströmformer:

• Uses Nyström method to approximate attention matrix
• Balances accuracy and efficiency
• Economic impact: Good trade-off for medium-length sequences

Collective Economic Implications:

• Training savings: Substantial for long-context applications
• Inference benefits: Reduced memory bandwidth and compute requirements
• Accuracy trade-offs: Vary by approach and task; some applications show minimal quality loss
• Best suited for: Applications where very long context is needed but precise token-level interactions are less critical

7.3 Mixture of Experts (MoE) and Sparse Activation

While MoE architectures were touched on in Chapter 3, advanced variants and economic implications warrant deeper examination:

7.3.1 Beyond Basic MoE: Advanced Routing and Expert Design

Innovations in Expert Architecture:

• Hierarchical MoE: Experts organized in trees for efficient routing
• Expert specialization: Training experts for specific linguistic phenomena or tasks
• Capacity factors: Dynamic adjustment of expert utilization based on load
• Expert choice routing: Tokens can select multiple experts rather than fixed top-k
• Learnable routing: More sophisticated router networks that adapt during training

Economic Implications:

• Training Efficiency:

- Active parameter count remains manageable while total model capacity grows

- Compute scales with active parameters, not total parameters

- Example: Switch Transformer with 1.6T parameters trains as if it were much smaller

• Inference Efficiency:

- Only activated experts compute, reducing effective compute per token

- Memory requirements still scale with total parameters (all experts must be loaded)

- Expert parallelism can distribute memory load across devices

• Memory Considerations:

- Total parameter storage still required (can be limiting factor)

- Expert parallelism helps but adds communication overhead

- Quantization becomes even more critical for large expert counts

• Scaling Laws:

- Different scaling characteristics than dense models

- Can achieve superior performance at fixed compute by increasing expert count

- Routing overhead becomes significant at very large expert counts

Applications and Token Economics:

• Massive scale models: Frontier research where absolute performance is paramount

- SCHOOL consideration: Potentially relevant for future foundation model development

- Token efficiency: High capability per token when expert routing is effective

• Multi-task and multi-domain models:

- Different experts specialize in different subjects or educational levels

- SCHOOL application: Mathematics expert, language expert, science expert, etc.

- Token efficiency: Better relevance and accuracy through expert specialization

• Adaptive computation:

- Routing can vary based on input difficulty

- Simple queries use fewer experts, complex ones engage more

- Token efficiency: Matches compute to problem difficulty

Adoption Considerations:

• Implementation complexity: Significant engineering effort for efficient routing
• Communication overhead: Expert distribution requires high-bandwidth interconnects
• Load balancing: Preventing expert under/over-utilization requires careful design
• Maturity: Well-established in research but production implementation remains challenging

7.3.2 Conditional Computation Beyond Experts

The MoE principle extends to other forms of conditional computation:

Sparse Mixture of Low-Rank Experts (S-MoE):

• Combines low-rank factorization with mixture of experts
• Further reduces parameter count while maintaining capacity
• Economic impact: Even better parameter efficiency than standard MoE

Activate Only What You Need (AWYN) Networks:

• Dynamically determine computation depth or width per token
• Examples: Mixture-of-Depths, Skipping Networks
• Economic impact: Compute scales with actual needed complexity, not worst-case
• Particularly effective for: Variable-complexity tasks like educational content generation

Token Routing and Early Exiting:

• Route tokens to different model paths based on complexity
• Exit early when sufficient confidence is reached
• Economic impact: Significant savings for batches with mixed difficulty examples
• SCHOOL application: Simple math facts get quick answers, complex problems engage deeper reasoning

7.4 Efficient Attention Mechanisms and Their Evolution

Building on Chapter 5's discussion of efficient attention, this section examines the latest advances:

7.4.1 FlashAttention-2 and Beyond

Continued Innovations:

• FlashAttention-2: Further optimizations for Hopper architecture, 2x speedup over FlashAttention-1
• Vertex Reordering: Improves memory access patterns for attention computation
• Block-sparse variants: Combine flash attention with structured sparsity
• Persistent kernels: Keep attention kernels resident on GPU to reduce launch overhead

Economic Implications:

• Training speedup: 2x-3x faster attention computation translates to reduced training time
• Inference throughput: Direct improvement in tokens/second for memory-bandwidth-bound workloads
• Energy efficiency: Less compute and memory movement for same output quality
• Enables longer contexts: Makes previously infeasible context lengths practical

7.4.2 Linear and Sub-quadratic Alternatives

Research continues on attention mechanisms with better than quadratic scaling:

Logarithmic Attention:

• Achieves O(n log n) or better complexity through hierarchical approaches
• Economic impact: Enables extremely long contexts with manageable compute

Constant Attention Approximations:

• Certain structural assumptions enable O(1) or O(log n) attention
• Economic impact: Potential for context-length-independent computation
• Limitation: Often requires specific data properties or approximations

Hybrid Approaches:

• Combine exact attention for nearby tokens with approximations for distant tokens
• Economic impact: Balances quality and efficiency
• Example: Local attention + global tokens or learned sparse patterns

7.5 Memory-Augmented and Retrieval-Augmented Architectures

Instead of storing all knowledge in parameters, these architectures combine parametric models with external memory:

7.5.1 Retrieval-Augmented Generation (RAG) and Variants

Beyond Basic RAG:

• Real-time RAG: Updating retrieval index continuously rather than periodic batches
• Hierarchical retrieval: Coarse-to-fine search for efficiency
• Learnable retrievers: Retrieval mechanism trained end-to-end with generator
• RAG with iteration: Multiple retrieve-generate cycles for refinement
• Atlas: Fusion of retrieval outputs in encoding space

Economic Implications:

• Parameter efficiency: Smaller parametric model achieves same or better performance

- Knowledge stored externally reduces model size requirements

- Example: 8B parameter RAG system can match 32B dense model on knowledge-intensive tasks

• Training costs:

- Smaller model = reduced training compute

- Retrieval system training often cheaper than parametric model scaling

- Index build costs amortized over many queries

• Inference costs:

- Retrieval adds latency but reduces generation compute

- Trade-off depends on retrieval efficiency and generation length

- Particularly effective when knowledge is sparse or frequently updated

• Memory/storage trade-off:

- Shift from VRAM/storage for model parameters to storage for index

- Index storage often cheaper than equivalent VRAM

- Enables scaling knowledge separately from reasoning capacity

Applications and Token Economics:

• Knowledge-intensive educational applications:

- SCHOOL application: Up-to-date curriculum information, latest scientific findings

- Token efficiency: Less parametric knowledge needed, more retrieved as needed

• Reducing hallucinations:

- Grounding in verified sources decreases factually incorrect outputs

- Fewer correction cycles = less wasted token generation

• Dynamic knowledge bases:

- Easy updating without retraining entire model

- Enables rapid incorporation of new educational standards or findings

7.5.2 Memory Networks and Neural Turing Machines

Differentiable Neural Computers (DNC) and Variants:

• External memory with learnable read/write operations
• Economic impact: Separates memory capacity from compute parameters
• Long-term dependency handling: Excellent for tasks requiring precise long-range recall

Economic Implications:

• Parameter efficiency: Reasoning network can be smaller; knowledge in external memory
• Training complexity: More complex training procedure but potentially less data needed
• Inference characteristics: Predictable memory access patterns
• Use case dependent: Shines on tasks requiring precise manipulation of information

Applications and Token Economics:

• Precise information tasks: Mathematical derivation, chemical equation balancing

- SCHOOL application: Step-by-step problem solving with exact intermediate values

- Token efficiency: Less parametric memorization of procedures, more dynamic lookup

• Structured output generation: Tables, diagrams, structured data

- External memory facilitates structured manipulation

- Reduces token waste from formatting errors or retries

7.6 Neuromorphic and Event-Driven Approaches

Taking inspiration from biological neural networks, these architectures process information differently:

7.6.1 Spiking Neural Networks (SNNs) for AI

Core Concept:

• Information encoded in timing of spikes rather than continuous values
• Asynchronous, event-driven processing
• Extremely energy-efficient for sparse activity patterns

Economic Implications:

• Training Challenges:

- Non-differentiable spiking complicates gradient-based training

- Surrogate gradient methods and conversion approaches emerging

- Currently higher training costs for equivalent performance

• Inference Efficiency:

- Potential orders-of-magnitude better energy efficiency for sparse workloads

- Particularly effective when inputs are naturally event-based or sparse

- Minimal energy consumption during idle periods

• Hardware Requirements:

- Neuromorphic chips (Intel Loihi, IBM TrueNorth) still emerging

- Simulation on conventional hardware loses efficiency benefits

- Long-term promise if specialized hardware becomes widely available

Applications and Token Economics:

• Sensory processing applications: Audio, vision, touch-based educational tools

- SCHOOL consideration: Interactive science labs with real-time sensor processing

- Token efficiency: Event-based processing may reduce need for constant token streams

• Always-on monitoring: Passive observation of learning environments

- Extremely low power enables continuous operation

- Token generation only when significant events occur

• Edge and IoT educational devices:

- Battery-operated or energy-harvesting educational tools

- Enables deployment in resource-constrained settings

7.6.2 Liquid State Machines and Reservoir Computing

Core Concept:

• Fixed, random recurrent reservoir provides high-dimensional dynamics
• Only readout weights are trained
• Economic impact: Dramatically reduced training complexity

Economic Implications:

• Training Efficiency:

- Only readout layer requires training (often linear regression)

- Reservoir fixed and randomly initialized (no backpropagation through time)

- Training can be orders of magnitude faster

• Inference Characteristics:

- Fixed reservoir computation + simple readout

- Predictable latency and memory usage

- Hardware implementation possible on analog or mixed-signal substrates

• Limitations:

- Performance ceiling lower than fully trained recurrent networks

- Best suited for temporal processing tasks rather than complex reasoning

Applications and Token Economics:

• Temporal pattern recognition: Speech phonemes, movement patterns, signal processing

- SCHOOL application: Pronunciation feedback, experimental data analysis

- Token efficiency: Efficient processing of temporal sequences

• Real-time forecasting: Short-term predictions in educational simulations

- Low latency enables responsive interactive simulations

- Minimal computational overhead for continuous operation

7.7 Architectures for Specific Modalities and Tasks

While general-purpose architectures remain important, specialized designs continue to emerge:

7.7.1 Code-Specialized Architectures

Innovations:

• Syntax-aware transformers: Incorporate abstract syntax tree (AST) information
• Execution-guided training: Use compiler/interpreter feedback during training
• Structure-informed attention: Bias attention toward syntactically relevant tokens
• Example architectures: CodeTF, GraphCodeBERT, PLBART with enhancements

Economic Implications:

• Training efficiency:

- Better utilization of training data through structural priors

- May achieve code understanding with less data or compute

- Reduced need for massive scale to capture programming language nuances

• Inference quality:

- Higher correctness for code generation tasks

- Fewer syntax errors = less wasted generation and retry attempts

- Better alignment with developer expectations

• Token economics:

- Higher quality output per token for code-related tasks

- Enables smaller models to achieve useful code assistance

- Particularly valuable for educational coding environments

Applications for SCHOOL:

• Programming education: Intelligent coding assistants for students

- Better error detection and correction suggestions

- More accurate code completion and explanation

- Token efficiency: More helpful output per token, less frustration

• Automated grading: Structured feedback on student code submissions

- More consistent and accurate assessment

- Reduced need for human intervention on common errors

7.7.2 Reasoning and Math-Specialized Architectures

Innovations:

• Process supervision: Training on intermediate reasoning steps, not just final answers
• Verifier models: Separate models trained to validate reasoning chains
• Tool use integration: Learning to use external calculators, symbolic solvers, etc.
• Symbolic-neural hybrids: Combining neural networks with symbolic reasoning engines

Economic Implications:

• Training considerations:

- Process supervision requires more detailed training data

- May increase data acquisition costs but reduce needed scale

- Verifier models add parameters but improve reliability

• Inference quality and efficiency:

- Fewer reasoning errors = less backtracking and correction

- Tool use can dramatically reduce compute for certain operations

- Example: Using calculator for arithmetic instead of neural approximation

• Token economics:

- Higher-quality reasoning chains per token

- More reliable educational outputs reduce need for verification cycles

- Tool integration shifts computation from neural to more efficient substrates

Applications for SCHOOL:

• Mathematical problem solving: Step-by-step algebra, calculus, proofs

- Process supervision improves pedagogical quality of explanations

- Tool use enables accurate computation where neural approximation fails

- Token efficiency: More correct steps per token, less wasted backtracking

• Science education: Chemical balancing, equation solving, formula derivation

- Hybrid approaches handle symbolic manipulation better than pure neural

- Reduces hallucinations in technical domains

7.7.3 Multimodal Fusion Architectures

Beyond simple concatenation or cross-attention, advanced fusion strategies emerge:

Innovations:

• Modality-specific processing: Specialized encoders for each modality type
• Adaptive routing: Dynamic computation allocation based on modality importance
• Factorized representations: Separate processing of shared and modality-specific features
• Example architectures: Flamingo variants, BLIP-2, Llama-adapter with enhancements

Economic Implications:

• Training efficiency:

- Modality-specific pretraining can reduce joint training requirements

- Better utilization of unimodal data (abundant and cheaper)

- Reduced need for expensive multimodal datasets

• Inference characteristics:

- Modality-dependent computation enables efficiency

- Example: Text-only queries use less compute than video-containing queries

- Modality dropout possible when certain inputs absent

• Token economics:

- Better alignment between input modalities and output quality

- Reduced token waste from misaligned or ineffective multimodal processing

- Enables more sophisticated educational content generation

Applications for SCHOOL:

• Rich educational content: Interactive lessons with text, diagrams, audio, video

- Better integration of modalities creates more engaging learning experiences

- Token efficiency: More educational value per token through effective multimodality

• Accessibility features:

- Automatic generation of alternative representations (alt text, captions, transcripts)

- Enables inclusive educational experiences

- Token efficiency: Single model serves multiple accessibility needs

• Science and language learning:

- Visual experiments, pronunciation guides, cultural context visuals

- Better learning outcomes through complementary modalities

7.8 Quantization, Sparsity, and Representation Efficiency

Advanced techniques for reducing the storage and computational footprint of models:

7.8.1 Beyond Standard Quantization

Innovations in Numerical Representation:

• Non-uniform quantization: Different precision for different parts of the model
• Logarithmic number systems: Efficient representation of wide dynamic ranges
• Stochastic rounding: Reduces quantization bias in training
• FP8 standardization: Emerging IEEE standard for 8-bit floating point
• Example: NVIDIA's FP8 format (E4M3 and E5M2 variants) gaining adoption

Economic Implications:

• Training:

- FP8 enables training with half the memory bandwidth of FP16

- Potential for training larger models on fixed hardware

- Requires careful handling of numerical stability

• Inference:

- 2x improvement over FP16, 4x over BF16 in memory bandwidth and storage

- Particularly beneficial for memory-bandwidth-bound workloads

- Enables larger models or longer contexts on fixed resources

• Hybrid approaches:

- Different precisions for different model components (e.g., attention vs. FFN)

- Optimizes for where precision matters most

7.8.2 Advanced Sparsity Techniques

Moving Beyond Unstructured Pruning:

• Structured sparsity patterns:

- N:M sparsity (e.g., 2:4) supported by Ampere and later GPUs

- Block sparsity, channel sparsity, head sparsity

- Hardware-friendly patterns enabling actual compute savings

• Training-time sparsity:

- Sparsification during training rather than pruning after

- Methods like RigL, SET, SNIP maintain sparse connectivity throughout

- Can achieve higher effective sparsity than post-training pruning

• Dynamic sparsity:

- Activation sparsity varies with input (Mixture of Experts is one form)

- Input-dependent computation pathways

- Matches compute to actual needed complexity

Economic Implications:

• Storage savings:

- Actual memory reduction from sparse storage formats

- Enables larger models within fixed memory budgets

- Particularly effective when combined with quantization

• Compute savings:

- Structured sparsity enables real FLOPS reduction on supported hardware

- Training savings from sparse forward/backward passes

- Inference benefits from reduced activation computation

• Accuracy retention:

- Structured and training-time sparsity often better preserves accuracy

- Allows higher sparsity targets than unstructured approaches

- Enables favorable quality/efficiency trade-offs

7.8.3 Low-Rank and Tensor Factorization

Advanced Matrix Approximation Techniques:

• Singular Value Decomposition (SVD) variants:

- Truncated SVD for compression

- Online or incremental SVD for adaptation

• Tensor decompositions:

- CP decomposition, Tucker decomposition, Tensor Train (TT) format

- Exploit multi-dimensional structure of weight tensors

• Kron decomposition:

- Kronecker product approximation for structured weight matrices

- Particularly effective for certain layer types

• Low-rank adaptation (LoRA) and variants:

- Train low-rank updates rather than full model

- Enables efficient adaptation and specialization

Economic Implications:

• Parameter efficiency:

- Dramatic reduction in stored parameters

- Example: LoRA adapters often <1% of base model size but effective for adaptation

- Enables rapid specialization without full retraining

• Training efficiency:

- Adaptation training much faster than full model training

- Particularly valuable for personalized or rapidly changing use cases

- Reduces barrier to entry for customization

• Inference characteristics:

- Adapter application adds minimal overhead

- Enables efficient switching between specialized behaviors

- Base model sharing reduces total storage footprint

• Applications for SCHOOL:

- Rapid adaptation to curriculum changes or teaching styles

- Personalized learning paths through lightweight specialist adapters

- Shared base model reduces deployment costs across variants

7.9 Economic Synthesis: How Advanced Architectures Change the Cost Curve

Understanding the economic implications requires synthesizing how these advances affect fundamental cost relationships:

7.9.1 Impact on Training Economics

Advanced architectures alter the training cost landscape in several ways:

Shifting the Compute-Data-Parameter Trade-off:

• Traditional transformers: Performance ∝ f(compute, data, parameters)
• Advanced architectures: May change the functional form f( )
• Examples:

- Linear attention: Reduces compute dependence on sequence length

- MoE: Decouples total parameters from active compute

- Memory-augmented: Separates knowledge storage from reasoning parameters

- Quantization/sparsity: Reduces effective parameter size

New Scaling Relationships:

• Some architectures follow different scaling laws than standard transformers
• May achieve similar performance with less compute for specific tasks
• Changing the "compute efficiency" axis of the scaling landscape
• Particularly beneficial for long-context, knowledge-intensive, or structurally-complex tasks

Data Efficiency Improvements:

• Architectural priors can reduce data requirements
• Example: Syntax-aware code models need less data to learn programming structure
• Memory-augmented approaches can leverage external knowledge stores
• Changing the "data efficiency" axis: More performance per token of training data

Practical Implications for Training Budget Allocation:

• For fixed training budget, advanced architectures may enable:

- Larger effective models (more parameters or capacity)

- Longer training runs (more tokens or steps)

- Better performance on specific task types

- Reduced need for extreme scale to achieve target quality

7.9.2 Impact on Inference Economics

Advanced architectures reshape inference economics through multiple channels:

Token Consumption Efficiency:

• Higher quality output per token reduces need for regeneration or correction
• Better alignment of computation with task requirements
• Example: Reasoning-specialized architectures produce fewer logical errors per token
• Impact: Effective cost per useful output decreases

Computation and Memory Efficiency:

• Reduced FLOPS per token lowers compute costs
• Reduced memory bandwidth per token alleviates the Chapter 2 bottleneck
• Lower memory footprint enables better hardware utilization
• Impact: More tokens per second per dollar of hardware

Latency and User Experience Improvements:

• Consistent latency improves predictability and satisfaction
• Enables new interaction paradigms (streaming, real-time collaboration)
• Reduced frustration from slow or inconsistent responses
• Impact: Higher retention and engagement, better learning outcomes

Deployment Flexibility:

• Enables deployment on previously infeasible hardware (edge, mobile, low-power)
• Opens new markets and use cases
• Reduces infrastructure costs for certain scenarios
• Impact: Expanded addressable market and improved ROI on AI investment

7.9.3 Strategic Considerations for Architecture Selection

Choosing among architectural options requires balancing multiple factors:

Task-Match Analysis:

• How well does the architecture align with your specific use cases?
• Example: Mamba/RetNet for long-context understanding, MoE for massive scale
• Consider: Input/output characteristics, complexity patterns, knowledge requirements

Ecosystem Maturity Evaluation:

• How available are tools, libraries, and community support?
• Example: Quantization has excellent tooling; neuromorphic still emerging
• Consider: Development time, debugging support, hiring availability
• Particularly important for educational technology with limited AI specialization

Implementation and Integration Costs:

• What engineering effort is required to adopt and maintain?
• Consider:

- Refactoring existing codebases

- New failure modes and debugging requirements

- Monitoring and observability adaptations

- Team training and knowledge transfer

• Often underestimated but critical for real-world economics

Risk-Reward Profiles:

• Established vs. cutting-edge trade-offs
• Example: Standard transformer with quantization vs. novel architecture
• Consider:

- Performance guarantees and predictability

- Long-term support and evolution prospects

- Opportunity cost of not adopting

- Potential competitive advantage from early adoption

Temporal Dynamics:

• How will the architecture's advantages evolve over time?
• Some benefits may diminish as competing techniques improve
• Others may compound as ecosystem and tooling mature
• Consider: Amortization horizon and expected technology lifecycle

7.10 Case Study: Advanced Architectures for SCHOOL (Pty) Ltd

Applying these concepts to our primary case study reveals specific opportunities and considerations:

7.10.1 Near-Term Opportunities (0-12 months)

High-Likelihood, High-Impact Advances:

1. Adoption of FlashAttention-2 and PagedAttention optimizations

- Immediate 2x-3x throughput improvement for existing transformer models

- Minimal code changes required

- Particularly beneficial for SCHOOL's context-heavy educational applications

2. Expanded use of quantization (FP8, INT4) and LoRA adapters

- Enables deployment of more capable models on fixed hardware

- Supports rapid adaptation to curriculum changes

- Reduces costs across development, testing, and production environments

3. Implementation of efficient KV cache management (PagerAttention style)

- Critical for long-generation tasks like essay writing or lesson planning

- Reduces memory waste and enables longer coherent outputs

- Directly improves token efficiency for generative educational use cases

4. Exploration of RetNet for specific generation-heavy workloads

- Potential for consistent low-latency responses in tutoring applications

- Simple implementation facilitates experimentation

- Particularly valuable for real-time educational interactions

7.10.2 Medium-Term Opportunities (12-24 months)

Promising Advances Requiring More Investment:

1. Hybrid architectures combining retrieval with parametric models

- For knowledge-intensive subjects (science, history, current events)

- Reduces need to parametrically store frequently changing information

- Enables up-to-date educational content without constant retraining

2. Specialized architectures for reasoning and mathematical tasks

- Process supervision and tool use integration for math education

- Improves correctness and reduces hallucinations in technical domains

- Addresses key pain point in educational AI: reliable quantitative reasoning

3. Advanced sparsity patterns (N:M, structured) combined with quantization

- Further pushes the efficiency frontier for deployment

- Particularly beneficial for scaling to larger user bases

- Enables more sophisticated models within fixed infrastructure budgets

4. Investigation of state space models (Mamba) for long-context educational processing

- Analyzing full student histories, longitudinal progress tracking

- Enables sophisticated personalized learning recommendations

- Addresses limitation of current models in handling extensive educational histories

7.10.3 Long-Term Horizon (24+ months)

Higher-Risk, Higher-Reward Possibilities:

1. Neuromorphic or event-driven approaches for specific sensory educational applications

- Interactive science labs, language pronunciation feedback, etc.

- Potential for extreme energy efficiency in always-on monitoring

- Enables novel educational form factors and deployment scenarios

2. Foundational model development using advanced architectural principles

- Building SCHOOL-specific foundation models optimized for educational tasks

- Potential to create models that outperform general-purpose alternatives

- Strategic investment for long-term differentiation and value creation

3. Participation in or leadership of educational AI architecture consortia

- Shaping standards and best practices for the educational AI community

- Access to shared research and pre-competitive advances

- Positions SCHOOL as a thought leader in educational technology

7.11 Conclusion

Advanced architectural innovations represent a critical lever for improving the economics of AI systems. By fundamentally altering how computation, memory, and data interact, these advances can shift the cost curves that govern training and inference economics.

For organizations like SCHOOL (Pty) Ltd, the strategic implications are clear:

1. Immediate wins: Readily available optimizations (better attention mechanisms, quantization, efficient caching) offer significant economic benefits with minimal risk

2. Targeted investments: Specialized architectures for reasoning, long-context understanding, or multimodal educational content can address specific value propositions

3. Ecosystem awareness: Architecture choices affect and are affected by tooling, community support, and talent availability

4. Long-term positioning: Some advances may enable strategic differentiation or new educational paradigms

5. Continuous evaluation: The architectural landscape evolves rapidly; regular reassessment is essential

The key insight is that architectural innovation isn't just about technical superiority—it's about economic transformation. The most valuable advances are those that change the fundamental relationships between compute, data, parameters, and output quality, thereby altering what's economically possible in AI systems.

As we move to the next chapter on regulatory risk and governance, we'll examine how external factors constrain and shape these economic possibilities, completing our analysis of the forces that determine AI viability and scalability.

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Chapter 8: Regulatory Risk and Governance

Navigating the Legal and Ethical Landscape of AI Economics

8.1 Introduction to Regulatory Risk in AI

While previous chapters have focused on internal economic factors—training costs, inference optimization, architectural choices, and ecosystem effects—this chapter examines the external forces that significantly impact AI economics: regulatory frameworks, governance requirements, and ethical considerations. These external factors can impose substantial costs, create barriers to entry, or conversely, create opportunities for organizations that navigate them effectively.

For organizations like SCHOOL (Pty) Ltd operating in the educational technology space, regulatory compliance is particularly critical due to the sensitive nature of educational data, the vulnerability of minor users, and the high standards for educational quality and accessibility. Failure to comply with regulations can result in severe financial penalties, reputational damage, and operational restrictions that far exceed the direct costs of compliance.

This chapter examines the key regulatory and governance challenges affecting AI economics, with particular attention to how they impact token economics and computational infrastructure costs. We'll explore data privacy regulations, AI-specific legislation, intellectual property considerations, accessibility requirements, and emerging governance frameworks, analyzing both their costs and their potential strategic advantages.

8.2 Data Privacy and Protection Regulations

Perhaps the most immediate and impactful regulatory domain for AI systems is data privacy, which directly affects how organizations can collect, store, process, and utilize the data that fuels AI models.

8.2.1 Major Privacy Frameworks Affecting AI

General Data Protection Regulation (GDPR) - European Union:

• Territorial Scope: Applies to any organization processing data of EU residents, regardless of where the organization is located
• Key Requirements:

- Lawful basis for processing (consent, contract, legal obligation, vital interests, public task, or legitimate interests)

- Data minimization and purpose limitation

- Rights to access, rectification, erasure ("right to be forgotten"), restriction, portability, and objection

- Data protection impact assessments (DPIAs) for high-risk processing

- Data protection officers (DPOs) for certain organizations

- Breach notification within 72 hours

- Significant fines: up to €20 million or 4% of global annual turnover, whichever is higher

• AI-Specific Implications:

- Training data collection requires lawful basis

- Model outputs may constitute personal data if they can be linked to individuals

- Automated decision-making provisions (Article 22) affect AI-driven assessments or recommendations

- Profiling restrictions particularly relevant for educational tracking systems

California Consumer Privacy Act (CCPA/CPRA) - United States:

• Scope: Applies to for-profit businesses doing business in California that meet certain thresholds
• Key Requirements:

- Right to know what personal information is collected

- Right to delete personal information

- Right to opt-out of sale of personal information

- Right to non-discrimination for exercising privacy rights

- Right to correct inaccurate personal information (CPRA)

- Expanded definition of "personal information" to include household data

- Fines: up to $7,500 per intentional violation

• AI-Specific Implications:

- Student data used for model training or improvement falls under CCPA

- Educational AI services may be considered "selling" data if they share insights with third parties

- Opt-out mechanisms must be provided for data uses beyond core educational service

Children's Online Privacy Protection Act (COPPA) - United States:

• Scope: Applies to operators of online services directed to children under 13 or who have actual knowledge they are collecting personal information from children under 13
• Key Requirements:

- Verifiable parental consent before collecting personal information from children

- Clear and comprehensive privacy policy

- Ability for parents to review and delete children's personal information

- Data security requirements

- Limitations on marketing to children

- Fines: up to $50,120 per violation (as of 2024)

• AI-Specific Implications:

- Critical for SCHOOL if serving K-8 educational market

- Parental consent mechanisms required for data collection

- Restrictions on behavioral profiling of children

- Limitations on using child data for product improvement without consent

Other Significant Privacy Regulations:

• Personal Information Protection and Electronic Documents Act (PIPEDA): Canada's federal private sector privacy law
• Lei Geral de Proteção de Dados (LGPD): Brazil's comprehensive data protection law
• Protection of Personal Information Act (POPIA): South Africa's data protection law
• Various state-level laws: Virginia (VCDPA), Colorado (CPA), Connecticut (CTDPA), Utah (UCPA), etc.

8.2.2 Economic Impact of Privacy Compliance

Privacy compliance creates both direct costs and indirect economic effects:

Direct Compliance Costs:

• Legal and Consulting Fees: $50k-$500k+ for initial compliance assessment and ongoing counsel
• Technology Investments:

- Data mapping and inventory tools

- Consent management platforms

- Data subject request (DSR) automation systems

- Encryption and pseudonymization solutions

- Privacy impact assessment software

• Operational Overhead:

- Dedicated privacy staff (DPO, privacy analysts)

- Training programs for employees

- Incident response planning and testing

- Audit and monitoring systems

• Process Changes:

- Additional steps in data collection workflows

- Modified development lifecycles (privacy by design)

- Extended timelines for feature releases

- Data retention and deletion procedures

Indirect Economic Effects:

• Innovation Velocity:

- Slower experimentation due to compliance reviews

- Reduced ability to repurpose data for unexpected uses

- Need for anonymization or synthetic data generation

• Data Utility Reduction:

- Purpose limitation restricts secondary uses of collected data

- Data minimization may reduce effectiveness of personalization

- Retention limits affect long-term model improvement capabilities

• User Trust and Adoption:

- Strong privacy practices can increase user trust and adoption rates

- Transparency about data use can be a competitive advantage

- Privacy features can justify premium pricing in certain segments

Impact on Token Economics Specifically:

• Data Collection Costs:

- Consent management adds friction to data acquisition

- May require incentives or explanations that reduce participation rates

- Effective cost per usable training token increases

• Model Training Limitations:

- Restrictions on using certain data types (e.g., biometric, location)

- Need for data cleaning and anonymization pipelines

- Reduced effective dataset size increases required raw data collection

• Inference-Time Privacy:

- Real-time privacy filtering adds computational overhead

- May require on-device processing to avoid data transmission

- Encryption/decryption costs affect inference efficiency

• Data Subject Requests:

- Cost of locating and deleting/user data from trained models

- Potential need for machine unlearning techniques

- Model retraining costs when training data must be removed

8.2.3 Privacy-Preserving AI Techniques and Their Economics

To mitigate privacy costs while maintaining data utility, organizations can employ specialized techniques:

Federated Learning:

• How it Works: Train models across decentralized devices without exchanging raw data
• Economic Implications:

- Reduced Central Data Costs: Less need for costly data centralization and storage

- Increased Communication Overhead: Model updates require bandwidth and synchronization

- Heterogeneity Challenges: Non-IID data across devices can reduce convergence efficiency

- Device Costs: Participating devices bear computation and battery costs

- Net Effect: Often reduces net costs when data sensitivity is high and device resources are available

Differential Privacy:

• How it Works: Add mathematical noise to data or queries to prevent individual identification
• Economic Implications:

- Utility-Privacy Trade-off: Increased noise reduces data quality and model performance

- Compensation Required: May need more data or longer training to achieve target performance

- Implementation Overhead: Privacy budget management and noise calibration

- Token Economics Impact: May require more tokens for equivalent output quality

Homomorphic Encryption and Secure Multi-Party Computation:

• How it Works: Perform computations on encrypted data without decryption
• Economic Implications:

- Significant Computational Overhead: Orders of magnitude slower than plaintext computation

- Specialized Hardware Requirements: May need accelerators or specialized implementations

- Limited Applicability: Currently impractical for large-scale LLM training/inference

- Future Potential: May become viable for specific high-sensitivity applications

Synthetic Data Generation:

• How it Works: Generate artificial data that mimics statistical properties of real data
• Economic Implications:

- Generation Costs: Computational resources to create synthetic datasets

- Quality Risks: Synthetic data may not capture critical real-world patterns

- Privacy Guarantees: Strong when properly implemented

- Hybrid Approaches: Combine real and synthetic data to balance utility and privacy

8.2.4 Strategic Approach for SCHOOL (Pty) Ltd

Given SCHOOL's focus on educational technology, particularly if serving minors, a proactive privacy strategy is essential:

Recommended Privacy Framework:

1. Privacy by Design: Integrate privacy considerations into all stages of AI development

2. Data Minimization: Collect only data essential for educational outcomes

3. Purpose Limitation: Clear, specific purposes for data use with prohibitions on secondary uses

4. Transparency: Clear privacy notices and consent mechanisms appropriate for age groups

5. Security: Appropriate technical and organizational measures for data protection

6. User Rights: Accessible mechanisms for data access, correction, deletion, and portability

7. Accountability: Documentation, training, and auditing to demonstrate compliance

Educational-Specific Considerations:

• Parental Consent Systems: Age-appropriate consent mechanisms with parental oversight
• Educational Purpose Exceptions: Leverage permissible educational uses under regulations
• Data Retention Policies: Align with educational record-keeping requirements
• Profiling Restrictions: Avoid automated profiling that could affect educational opportunities
• Special Education Considerations: Additional protections for sensitive disability-related data

Cost-Benefit Balance:

While privacy compliance adds costs, it also creates opportunities:

• Trust Premium: Schools and parents may pay more for provably safe educational AI
• Market Access: Compliance enables operation in regulated markets (EU, California, etc.)
• Reduced Liability: Lower risk of costly fines, lawsuits, and reputational damage
• Competitive Differentiation: Privacy leadership can be a market differentiator
• Long-term Sustainability: Avoids costly retrofits and disruptions from non-compliance discoveries

8.3 Emerging AI-Specific Legislation

Beyond general privacy laws, jurisdictions are developing AI-specific regulations that directly impact AI development and deployment:

8.3.1 European Union AI Act

Overview: The world's first comprehensive AI-specific regulatory framework, using a risk-based approach. Risk Categories and Requirements:

• Unacceptable Risk (Prohibited):

- Social scoring by governments

- Real-time biometric identification in public spaces

- Subliminal techniques to distort behavior

- Exploiting vulnerabilities of specific groups (age, disability, etc.)

- *Relevance to SCHOOL*: Certain student tracking or profiling applications may fall here

• High Risk (Stringent Requirements):

- Biometric identification and categorization

- Critical infrastructure management

- Educational and vocational training (including admissions, assessments)

- Employment worker management

- Access to essential private and public services

- Law enforcement and migration control

- *Relevance to SCHOOL*:

- Educational assessment tools likely classified as high-risk

- Adaptive learning systems may be high-risk if they significantly affect educational trajectories

- Requirements include: risk management systems, data governance, technical documentation,

transparency, human oversight, accuracy, robustness, and cybersecurity

• Limited Risk (Transparency Obligations):

- Chatbots, emotion recognition systems, biometric categorization

- *Relevance to SCHOOL*: Educational chatbots and feedback systems likely here

- Requirements: Disclose that users are interacting with AI

• Minimal Risk:

- AI-enabled video games, spam filters

- Most educational content - Mostly exempt from specific requirements

Economic Implications for SCHOOL:

• Compliance Costs for High-Risk AI:

- Conformity assessments (self-assessment or third-party depending on type)

- Technical documentation maintenance

- Post-market monitoring systems

- Quality management systems

- Registration in EU database

- Estimated cost: 5-15% of project budget for high-risk AI systems

• Impact on Development Velocity:

- Longer development cycles due to compliance requirements

- Need for compliance expertise in development teams

- Potential limitations on certain AI techniques in educational contexts

• Market Access Benefits:

- Legal ability to operate in EU market (major educational technology market)

- Competitive advantage over non-compliant providers

- Potential for "EU AI Act Compliant" certification as marketing asset

• Token Economics Effects:

- Increased computational overhead for logging, monitoring, and transparency features

- Potential need for more conservative model designs to ensure robustness

- Data governance requirements may affect training data availability and quality

- Human oversight requirements may increase labor costs per inference

8.3.2 United States AI Regulatory Landscape

Federal Initiatives:

• AI Bill of Rights (White House, 2022): Principles for protecting public in AI systems

- Safe and effective systems

- Algorithmic discrimination protections

- Data privacy

- Notice and explanation

- Human alternatives, consideration, and fallback

- *Status*: Guidance, not binding regulation (but influences agency actions)

• Executive Order on AI (Biden, 2023):

- Requires federal agencies to assess and manage AI risks

- Directs NIST to develop AI risk management framework

- Addresses AI in education specifically

- *Impact*: Shapes federal procurement and funding priorities

• Sector-Specific Agency Guidance:

- Department of Education: Guidance on AI in education

- FTC: Enforcement against deceptive AI claims

- FDA: Regulation of AI as medical device (relevant for some edtech)

- FCC: Communications aspects of AI systems

State-Level Initiatives:

• California:

- AB-331: Would regulate automated decision systems (including in education)

- Proposed regulations on deepfakes and synthetic media

- CPPA regulations on automated decision-making technology

• New York:

- Proposed AI regulation in hiring (Local Law 144)

- Considerations for AI in education settings

• Illinois:

- Artificial Intelligence Video Interview Act

- Biometric Information Privacy Act (BIPA) - significant for facial recognition in edtech

8.3.3 International and Multilateral Approaches

OECD AI Principles:

• Intergovernmental standard on responsible AI
• Influences national policies worldwide
• Principles: inclusive growth, sustainable development, human-centered values, transparency, robustness

UNESCO Recommendation on AI Ethics:

• First global framework on AI ethics
• Particularly relevant for educational applications
• Addresses: inclusivity, equity, gender equality, cultural diversity, education, research, culture

G7 and G20 AI Initiatives:

• Coordinated approaches to AI governance
• Focus on interoperability and common standards
• Relevant for multinational educational organizations

8.3.4 Impact on AI Development Economics

AI-specific legislation creates new cost categories and constraints:

Compliance Cost Categories:

• Risk Assessment and Classification: Determining risk level of AI systems
• Technical Documentation: Detailed records of data, training, testing, and performance
• Transparency and Explainability Features: Interfaces to show how AI works
• Human Oversight Mechanisms: Interfaces and procedures for human intervention
• Robustness and Accuracy Testing: Ongoing validation of model performance
• Cybersecurity Measures: Protection against attacks and manipulation
• Quality Management Systems: Processes to ensure consistent quality
• Post-Market Monitoring: Systems to track performance after deployment
• Registration and Reporting: Obligations to regulatory bodies
• Third-Party Assessments: Required certifications for certain risk levels

Ongoing Compliance Costs:

• Regular Audits: Periodic reviews of compliance status
• Update Management: Ensuring updates maintain compliance
• Incident Reporting: Systems for reporting and addressing compliance breaches
• Training Programs: Ongoing staff education on compliance requirements
• Legal Monitoring: Tracking regulatory changes and interpretations

Impact on Innovation and Time-to-Market:

• Increased Development Timelines: Compliance steps add time to product releases
• Reduced Experimentation Freedom: Limitations on certain techniques or data uses
• Need for Specialized Expertise: Hiring or training for AI compliance specialists
• Conservative Design Bias: Tendency toward safer, less innovative approaches to minimize risk

Strategic Advantages of Compliance:

• Market Access: Legal ability to operate in regulated jurisdictions
• Trust and Credibility: Demonstrated commitment to responsible AI
• Reduced Legal Risk: Lower probability of fines, lawsuits, and injunctions
• Investor Attractiveness: ESG and responsible investment considerations
• Public Procurement Eligibility: Many government contracts require compliance
• Competitive Moat: Difficulty for non-compliant competitors to enter market

8.4 Intellectual Property Considerations in AI

IP rights significantly affect the economics of AI systems, influencing what can be protected, how value can be captured, and what freedoms organizations have to operate.

8.4.1 Training Data and IP

Copyright Issues in Training Data:

• Scope of Protection: Literary works, artistic works, software, databases
• Training Data Concerns:

- Large language models trained on vast corpora containing copyrighted material

- Fair use defenses vary by jurisdiction and are legally uncertain

- Licensing requirements for certain types of content (news, images, code)

- Opt-out mechanisms for content owners (increasingly common)

• Economic Implications:

- Licensing costs for premium training data (books, journals, specialized content)

- Legal risk and potential damages from infringement claims

- Need for data filtering and licensing compliance systems

- Potential restrictions on model outputs that resemble training data too closely

Data Ownership and Rights:

• User-Generated Data: Who owns data entered by students or educators?
• Model Outputs: Who owns content generated by AI systems?
• Derivative Works: Status of AI-assisted creations
• Educational Context: Special considerations for works created in educational settings
• Economic Implications:

- Clear terms of service required to define ownership and usage rights

- Potential revenue sharing models for valuable user-generated content

- Licensing opportunities for valuable model outputs or derivatives

- Dispute resolution mechanisms for ownership conflicts

8.4.2 AI-Generated Content and IP

Copyrightability of AI Outputs:

• Human Authorship Requirement: Most jurisdictions require human authorship for copyright
• Current Status:

- US Copyright Office: AI-generated works without human authorship not copyrightable

- UK: Computer-generated works may have protection (50 years from creation)

- EU: Varies by member state, generally requires human creativity

- *Impact*: Pure AI-generated educational content may not be protectable

• Human-AI Collaboration:

- Works with meaningful human contribution may be protectable

- Threshold for "meaningful contribution" is legally uncertain

- Prompt engineering, selection, editing, and arrangement may qualify

• Economic Implications:

- Difficulty monetizing purely AI-generated educational content

- Value shifts to human curation, guidance, and educational context

- Business models may focus on services rather than content sales

- Increased importance of pedagogical design and teacher involvement

Patent Considerations for AI Systems:

• Subject Matter Eligibility:

- Abstract ideas, mathematical algorithms, and mental processes often excluded

- Specific applications of AI to technical problems may be patentable

- AI hardware innovations more likely to be patentable than pure software

• Inventorship Challenges:

- Determining human inventors in AI-assisted innovation

- AI systems themselves cannot be inventors (current patent law)

• Disclosure Requirements:

- Enabling others to reproduce the invention

- May conflict with trade secret protection for models or training data

• Economic Implications:

- Patent protection may be available for specific AI applications in education

- Trade secrets often more practical for protecting models and training data

- Defensive patenting to prevent litigation from others

- Licensing opportunities for patented AI educational technologies

8.4.3 Open Source and Licensing Dynamics

Model Licensing:

• Restrictive Licenses:

- Some model providers impose use restrictions (non-compete, field-of-use)

- May prohibit certain educational applications or require revenue sharing

- Example: Certain commercial models prohibit use in weapons development

• Permissive Licenses:

- MIT, Apache 2.0 allow broad use including commercial applications

- Require attribution but minimal restrictions

- Preferred for educational technology due to flexibility

• Copyleft Licenses:

- GPL requires derivative works to be similarly licensed

- May complicate proprietary educational products

- Less common in AI model releases

Training Data Licensing:

• Public Domain Works:

- Older texts, government publications, expired copyrights

- Increasingly valuable for training as costs rise for copyrighted material

- Projects like Project Gutenberg, Internet Archive, HathiTrust

• Creative Commons:

- Various licenses with different permissions (BY, SA, NC, ND combinations)

- Requires careful compliance with license terms

- CC0 (public domain dedication) particularly valuable for training

• Licensed Content:

- Traditional licensing agreements with publishers and rights holders

- Can be expensive but provides access to high-quality, relevant material

- Educational discounts often available

- Clearance processes add time and cost to data acquisition

Software and Tool Licensing:

• ML Frameworks:

- TensorFlow, PyTorch, JAX have permissive licenses

- Generally favorable for commercial educational use

• Annotation and Data Tools:

- Varying licenses affect cost of data preparation pipeline

- Open source options reduce costs but may have support limitations

• Deployment and Serving Tools:

- TensorFlow Serving, TorchServe, Triton Inference Server

- License considerations for commercial educational products

8.4.4 Strategic IP Approach for SCHOOL (Pty) Ltd

Given SCHOOL's educational focus, a balanced IP strategy is essential:

Recommended IP Framework:

1. Training Data Strategy:

- Prioritize public domain, Creative Commons, and licensed educational content

- Develop relationships with educational publishers for licensing agreements

- Implement systems to track data provenance and licensing compliance

- Consider synthetic data generation for sensitive or expensive-to-license areas

2. Model and Output Protection:

- Focus patent strategy on specific educational applications and systems

- Use trade secrets for model architectures and training methodologies where appropriate

- Develop clear terms of service defining ownership of user inputs and AI outputs

- Consider hybrid models where AI assists human-created educational content

3. Open Source Engagement:

- Strategic use of open source models where licensing permits educational use

- Contribute improvements back to communities when beneficial

- Balance open source use with need for proprietary differentiation

- Monitor license compatibility when combining components

4. Educational Exceptions and Fair Use:

- Leverage educational exceptions in copyright law where applicable

- Document fair use rationale for training data uses

- Implement systems to respect opt-out requests from content owners

- Consider licensing collectives for educational content access

Economic Impact Summary:

• Costs: Licensing fees, legal compliance, IP tracking systems, potential litigation
• Benefits: Clear ownership reduces disputes, enables revenue models, protects innovations
• Strategic Balance: Seek IP protection where it enables value capture without hindering educational mission
• Token Economics Effects:

- Licensing costs increase effective cost per training token

- Clear IP boundaries enable confident investment in model development

- Output ownership rules affect monetization strategies for AI-generated educational content

8.5 Accessibility and Inclusivity Requirements

Educational AI systems must comply with accessibility laws to ensure equal access for all learners, including those with disabilities.

8.5.1 Major Accessibility Frameworks

Americans with Disabilities Act (ADA) - United States:

• Title II: Public entities (state and local governments)
• Title III: Public accommodations and commercial facilities
• Relevance to SCHOOL:

- If providing services to public schools, likely covered by Title II

- If providing services directly to public, likely covered by Title III

- Requires equal access to goods, services, facilities, privileges, advantages, or accommodations

• Web Content Accessibility Guidelines (WCAG):

- De facto standard for web accessibility under ADA

- Current version: WCAG 2.1 (with 2.2 in draft)

- Four principles: Perceivable, Operable, Understandable, Robust (POUR)

- Three conformance levels: A (minimum), AA (recommended), AAA (highest)

- *Relevance*: Most educational AI interfaces will be web or app-based

Section 508 of the Rehabilitation Act - United States:

• Scope: Federal electronic and information technology
• Relevance:

- Applies to AI systems sold to or used by US federal agencies

- Educational AI used in federal schools or programs must comply

- Standards based on WCAG 2.0 AA

EN 301 549 - European Union:

• Scope: ICT products and services in Europe
• Relevance:

- Harmonized standard for public procurement in EU

- Based on WCAG 2.1 AA

- Required for selling to EU public sector entities

Accessible Canada Act (ACA) and Related Regulations:

• Scope: Federal sector under parliamentary jurisdiction
• Relevance: Similar WCAG-based requirements for accessibility

Convention on the Rights of Persons with Disabilities (CRPD):

• International Treaty: Ratified by many countries
• Article 9: Accessibility to physical environment, transportation, information, and communications
• Article 24: Inclusive education systems
• Influences: National accessibility legislation worldwide

8.5.2 Specific Accessibility Requirements for Educational AI

Perceivable:

• Text Alternatives: Alt text for images, captions for videos, transcripts for audio
• Adaptable Content: Content that can be presented in different ways without losing information
• Distinguishable: Sufficient color contrast, resizable text, audio control
• *AI Implications*:

- Image descriptions for visual content generated by AI

- Captioning and transcription for audio/video AI outputs

- Adjustable display preferences for text-based AI interactions

Operable:

• Keyboard Accessible: All functionality available via keyboard
• Enough Time: Adjustable timing, ability to pause, no time limits that disadvantage users
• Seizure Prevention: No content that causes seizures or physical reactions
• Navigable: Ways to help users navigate, find content, determine location
• *AI Implications*:

- Keyboard navigation for AI chatbots and interfaces

- Adjustable response timing for AI tutoring systems

- Clear navigation and help systems in AI educational platforms

Understandable:

• Readable and Understandable: Clear language, predictable input assistance
• Input Assistance: Help users avoid and correct mistakes
• *AI Implications*:

- Plain language options for AI-generated explanations

- Consistent and predictable AI behavior

- Error correction and guidance in AI interactions

- Multiple difficulty levels and scaffolding in educational content

Robust:

• Compatible: Maximize compatibility with current and future user tools
• *AI Implications*:

- Work with assistive technologies (screen readers, voice recognition, etc.)

- Future-proofing against changes in browsers and operating systems

- Standards-based interfaces that don't rely on specific technologies

8.5.3 Economic Impact of Accessibility Compliance

Direct Compliance Costs:

• Accessibility Audits: Manual and automated testing ($10k-$100k+)
• Remediation Work: Fixing identified accessibility issues
• Assistive Technology Testing: Testing with screen readers, voice recognition, etc.
• Training and Awareness: Educating development and content teams
• Accessibility Expertise: Hiring or consulting with accessibility specialists
• Ongoing Monitoring: Regular checks as content and features update

Design and Development Overhead:

• Increased Design Time: Accessibility considerations add to UI/UX design process
• Development Complexity: Additional coding for accessibility features
• Testing Requirements: More comprehensive testing matrices
• Content Creation: Accessible content creation may require additional steps

Indirect Economic Effects:

• Market Expansion:

- Access to approximately 15-20% of population with disabilities

- Educational institutions often require accessibility compliance

- Public funding frequently contingent on accessibility

• Innovation Stimulus:

- Accessibility constraints often drive better design for all users

- Example: Captioning benefits not just deaf users but also those in noisy environments

- Clearer interfaces benefit users with cognitive load and non-native speakers

• Brand and Reputation:

- Demonstrates commitment to equity and inclusion

- Can be a differentiator in educational markets

- Reduces risk of negative publicity and legal challenges

• User Experience Improvements:

- Often results in better usability for all users

- Reduced support costs from fewer usability issues

Impact on Token Economics Specifically:

• Additional Processing:

- Accessibility features may add computational steps (e.g., image description generation)

- May require additional model calls or processing stages

- Example: Generating alt text for every educational image created by AI

• Latency Considerations:

- Accessibility features must not create unreasonable delays

- May require optimization of accessibility processing paths

- Real-time captioning or transcription adds processing burden

• Output Diversity:

- May need to generate multiple formats (text, audio, simplified versions)

- Increases token consumption per educational concept

- But enables reach to broader audience, improving cost per reached learner

• Training Data Needs:

- May require diverse training data to ensure accessibility

- Example: Training on diverse speech patterns for better voice recognition

- Increases data acquisition and processing costs

8.5.4 Strategic Accessibility Approach for SCHOOL (Pty) Ltd

Given the educational mission, accessibility should be viewed as integral to the product, not just compliance:

Recommended Accessibility Framework:

1. Universal Design for Learning (UDL) Principles:

- Multiple means of representation (how information is presented)

- Multiple means of action and expression (how learners demonstrate knowledge)

- Multiple means of engagement (how learners are motivated and engaged)

- *Application*: Design AI systems to offer content and interaction in varied formats

2. WCAG 2.1 AA as Minimum Standard:

- Target AA compliance for all user-facing interfaces

- Consider AAA for particularly critical educational functions

- Integrate accessibility testing into continuous integration/deployment

3. Involve Users with Disabilities in Design:

- Participatory design with students, educators, and accessibility experts

- Testing with assistive technologies throughout development

- Feedback loops for continuous improvement

4. Accessibility Documentation and Transparency:

- Accessibility statements and conformance claims

- Voluntary Product Accessibility Templates (VPATs) for procurement

- Clear communication of accessibility features and limitations

5. Ongoing Commitment:

- Accessibility as ongoing process, not one-time checkbox

- Regular audits and updates as technologies and standards evolve

- Budget allocation for accessibility maintenance and improvement

Educational-Specific Considerations:

• Learning Disabilities: Dyslexia, dyscalculia, ADHD considerations
• Physical Disabilities: Motor impairments affecting interaction methods
• Sensory Disabilities: Visual and hearing impairments
• Cognitive Disabilities: Intellectual disabilities, autism spectrum considerations
• Multiple Disabilities: Intersectional needs requiring comprehensive approaches
• Assistive Technology Compatibility: Work with common edtech assistive tools

Cost-Benefit Analysis:

While accessibility adds costs, it provides significant benefits:

• Expanded Addressable Market: Reach learners who would otherwise be excluded
• Improved Educational Outcomes: Better engagement and learning for diverse learners
• Public Funding Eligibility: Many grants and contracts require accessibility
• Reduced Legal Risk: Avoid costly OCR complaints, lawsuits, and remediation demands
• Enhanced Reputation: Demonstrated commitment to educational equity
• Innovation Benefits: Accessibility-driven improvements often benefit all users

8.6 Governance Frameworks and Internal Controls

Beyond external regulations, effective internal governance is essential for managing AI risks and ensuring responsible development.

8.6.1 AI Governance Structures

Board and Executive Oversight:

• AI Ethics Committees: Cross-functional groups reviewing AI projects
• Risk Management Integration: AI risks incorporated into enterprise risk management
• Clear Accountability: Defined roles and responsibilities for AI governance
• Reporting Lines: Regular reporting to board or executive leadership on AI risks

Policy and Procedure Frameworks:

• AI Ethics Principles: Organizational statements on responsible AI development
• Acceptable Use Policies: Guidelines for appropriate AI use cases
• Data Governance Policies: Rules for data collection, storage, and usage
• Model Lifecycle Management: Standards for development, testing, deployment, and monitoring
• Third-Party AI Management: Guidelines for using external AI APIs and models
• Incident Response Plans: Procedures for addressing AI failures or harms

Technical Governance Mechanisms:

• Model Cards: Documentation of model capabilities, limitations, and appropriate use
• Data Sheets for Datasets: Transparency about training data characteristics
• System Cards: Broader view of AI systems in context of use
• Audit Trails: Logging of AI decisions and actions for accountability
• Explainability Interfaces: Tools to help users understand AI outputs
• Bias and Fairness Testing: Regular evaluation of model outputs for disparate impacts

Organizational and Cultural Elements:

• AI Literacy Training: Education for all employees on AI basics and risks
• Ethics Training: Specific training on ethical considerations in AI work
• Diversity and Inclusion: Ensuring diverse perspectives in AI development teams
• Psychological Safety: Environment where concerns can be raised without fear
• Continuous Learning: Systems to stay updated on evolving best practices

8.6.2 Economic Impact of AI Governance

Direct Governance Costs:

• Governance Body Operations: Time and resources for committees and meetings
• Policy Development and Maintenance: Creating and updating governance documents
• Training Programs: Initial and ongoing education on AI governance
• Technical Investments: Tools for model cards, data sheets, audit trails, explainability
• Testing and Validation: Additional validation steps for bias, fairness, robustness
• Third-Party Management: Processes for evaluating and monitoring external AI use

Process and Efficiency Effects:

• Development Timeline Increases: Governance reviews add time to product cycles
• Experimentation Constraints: Certain high-risk experiments may require additional approvals
• Documentation Overhead: Time spent on governance-related documentation
• Cross-Functional Coordination: Need for alignment between legal, technical, product teams

Risk Mitigation Benefits:

• Reduced Failure Rates: Fewer harmful AI deployments due to pre-release scrutiny
• Lower Incident Response Costs: Fewer incidents to manage when they occur
• Decreased Legal and Regulatory Risk: Demonstrated due diligence reduces penalties
• Improved Model Quality: Systematic testing leads to more reliable AI systems
• Enhanced Trust: Users and stakeholders gain confidence in AI systems
• Better Decision Making: Informed choices about AI development directions

Talent and Culture Benefits:

• Attraction of Ethical Talent: Professionals increasingly seek responsible workplaces
• Improved Employee Engagement: Pride in working on socially beneficial projects
• Reduced Turnover: Lower costs from retaining employees who value ethics
• Enhanced Reputation: Attracts customers, partners, and investors who prioritize responsibility
• Innovation Direction: Governance can steer innovation toward socially valuable applications

Impact on Token Economics Specifically:

• Development Costs:

- Governance overhead increases cost per model developed

- May reduce number of models developed per budget

- But increases likelihood that developed models are suitable and safe

• Inference Overhead:

- Explainability features add computational steps

- Logging and monitoring add processing burden

- But enables better troubleshooting and optimization

• Model Quality Improvements:

- Better models produce higher-quality output per token

- Reduces wasted generation from errors or inappropriate outputs

- Increases effective educational value per token

• Long-Term Cost Avoidance:

- Prevents costly recalls, reputational damage, and legal settlements

- Avoids need for expensive remediation of harmful AI deployments

- Reduces volatility in long-term operating costs

8.6.3 Recommended Governance Approach for SCHOOL (Pty) Ltd

Given SCHOOL's educational mission and potential service to minors, robust governance is essential:

Recommended Governance Framework:

1. AI Ethics Committee:

- Cross-functional representation (education, technology, legal, ethics, student/parent reps)

- Regular meetings with clear agendas and decision-making processes

- Authority to pause or modify projects based on ethical concerns

- Reporting to executive leadership and/or board

2. Comprehensive AI Policies:

- AI Ethics Principles aligned with educational mission and values

- Data Governance Policy covering student data lifecycle

- Model Development Lifecycle with clear stages and gatekeeping

- Acceptable Use Policy prohibiting harmful educational applications

- Third-Party AI Management Policy for external model and API use

- Incident Response Plan for AI failures or harms in educational contexts

3. Technical Governance Infrastructure:

- Standardized Model Card template for all AI models

- Data Sheet requirements for all training datasets

- Automated bias and fairness testing in CI/CD pipelines

- Explainability features for all user-facing AI systems

- Version control and audit trails for all AI-related work

4. Education and Culture Components:

- Mandatory AI literacy training for all employees

- Specialized training for educators on AI in education

- Ethics case studies and discussions integrated into regular meetings

- Anonymous channels for reporting concerns

- Recognition programs for responsible AI practices

5. Continuous Improvement:

- Regular policy reviews and updates

- Post-implementation reviews of deployed AI systems

- Benchmarking against evolving best practices and standards

- Feedback loops from users, educators, and affected communities

Educational-Specific Governance Considerations:

• Child Protection: Special protocols for AI interacting with minors
• Educational Validity: Processes to ensure AI outputs are pedagogically sound
• Equity and Inclusion: Ongoing assessment for disparate impacts across student groups
• Transparency with Stakeholders: Clear communication with schools, parents, and regulators
• Academic Integrity: Measures to prevent inappropriate use that undermines learning
• Teacher Empowerment: Positioning AI as tool to enhance, not replace, educators

Cost-Benefit Analysis:

While governance adds costs, it creates significant value:

• Reduced Catastrophic Risk: Avoids devastating failures that could end the organization
• Improved Product-Market Fit: Better alignment with educational needs and values
• Enhanced Stakeholder Trust: Schools, parents, and regulators more likely to adopt
• Attraction of Mission-Aligned Talent: Educators and technologists who value educational integrity
• Long-Term Sustainability: Avoids costly pivots and rebuilding from governance failures
• Educational Effectiveness: Better learning outcomes from well-designed, responsible AI

8.7 Case Study: Regulatory Approach for SCHOOL (Pty) Ltd

Applying regulatory concepts to our primary case study reveals a practical approach:

8.7.1 Current Regulatory Position

SCHOOL has likely implemented some basic compliance measures:

• Basic privacy notice and terms of service
• Basic security measures for data protection
• Initial terms of service for AI service use
• Some accessibility considerations in product design

Gaps and Opportunities:

1. Comprehensive Privacy Program: Particularly important if serving minors

2. AI-Specific Compliance Framework: Preparing for regulations like EU AI Act

3. Formal Accessibility Program: Systematic approach to WCAG compliance

4. Structured AI Governance: Beyond ad-hoc ethics considerations

5. IP Strategy Aligned with Educational Mission: Clear approach to training data and outputs

5. International Compliance Strategy: If planning to operate outside home jurisdiction

8.7.2 Phased Implementation Roadmap

Based on risk and opportunity, SCHOOL should consider:

Immediate (0-3 months):

1. Appoint Privacy and AI Ethics Officers: Even if part-time initially

2. Conduct Privacy Gap Analysis: Against GDPR, COPPA, and relevant local laws

3. Review and Update Terms of Service and Privacy Policy: For AI-specific considerations

4. Begin Accessibility Audit: Of current AI interfaces and user experiences

5. Establish Basic AI Ethics Principles: Aligned with educational mission

Short-term (3-6 months):

1. Implement Consent Management System: Particularly for COPPA/GDPR compliance if needed

2. Develop Data Inventory and Classification: Understand what data is collected and why

3. Formalize Data Retention and Deletion Procedures: Including handling of data subject requests

4. Implement Basic Accessibility Remediation: Address highest-priority issues from audit

5. Form AI Ethics Committee: With diverse stakeholder representation

Medium-term (6-12 months):

1. Implement Comprehensive Data Subject Request Process: For access, correction, deletion

2. Deploy Technical Privacy Controls: Encryption, pseudonymization, access controls

3. Complete Accessibility Remediation: To achieve WCAG 2.1 AA compliance

4. Implement Model Card and Data Sheet Processes: For all AI models

5. Establish Regular Bias and Fairness Testing: Particularly for educational equity impacts

Long-term (12-24 months):

1. Achieve Full Compliance Framework: For all relevant privacy and accessibility regulations

2. Participate in Industry Standards: Contribute to emerging AI governance best practices

3. Develop Explainability Features: Tailored to educational contexts and age groups

4. Implement Robust Audit Trail and Monitoring Systems: For AI systems in production

5. Seek External Certifications or Audits: For privacy, accessibility, and AI ethics

Ongoing:

1. Regular Policy Reviews and Updates: As regulations and technologies evolve

2. Continuous Training and Awareness: For all staff on compliance responsibilities

3. Monitoring Regulatory Developments: Particularly AI-specific legislation

4. Stakeholder Engagement: Particularly with schools, parents, and regulatory bodies

5. Continuous Improvement Cycle: Based on audits, feedback, and evolving best practices

8.7.3 Quantifying Regulatory Costs and Benefits

While precise quantification is challenging, estimates can inform decision-making:

Annual Compliance Cost Estimates (Mid-sized Educational AI Provider):

• Privacy Compliance: $50k-$200k (legal, technology, training, overhead)
• Accessibility Compliance: $30k-$150k (audit, remediation, testing, training)
• AI-Specific Compliance: $40k-$180k (governance, documentation, testing, overhead)
• IP Management: $20k-$100k (legal, licensing, tracking, enforcement)
• Total Estimated Annual Compliance Cost: $140k-$630k
• As Percentage of Revenue: Typically 5-20% for compliant educational technology providers

Benefit Estimates (Annualized):

• Market Access Value: $100k-$500k (ability to operate in regulated markets)
• Risk Reduction Value: $75k-$300k (avoided fines, legal costs, remediation)
• Trust and Adoption Value: $50k-$250k (increased conversion, retention, premium pricing)
• Operational Efficiency Value: $25k-$100k (reduced rework, better quality, fewer incidents)
• Innovation Guidance Value: $10k-$50k (better R&D alignment, reduced failed experiments)
• Total Estimated Annual Benefits: $255k-$1.4M
• Net Estimated Annual Value: $115k-$770k (positive in most scenarios)

Key Variables Affecting Outcome:

• Jurisdictional Scope: More jurisdictions = higher costs but also greater market access
• User Age Range: Serving minors significantly increases privacy compliance complexity
• Data Sensitivity: More sensitive data (health, biometrics, etc.) increases protection costs
• Public Funding Dependence: Higher dependence increases importance of compliance
• Competitive Landscape: In markets where competitors neglect compliance, advantages increase

8.8 Regulatory Risk and Token Economics: Synthesis

Understanding regulatory impacts transforms how we think about token economics in AI:

Beyond Simple Cost-per-Token Models:

Traditional token economics focuses narrowly on:

```

Cost per token = (Infrastructure cost + Energy cost + Overhead) / Tokens produced

```

Regulation-Enhanced Token Economics:

A more comprehensive model includes regulatory factors:

```

Effective cost per compliant educational outcome =

[(Infrastructure cost + Energy cost + Overhead)

+ Regulatory compliance costs

• Regulatory risk mitigation value

+ Regulatory-enabled market access value]

/ (Tokens produced × Compliance quality multiplier)

```

Where:

• Regulatory compliance costs include direct costs of meeting legal requirements
• Regulatory risk mitigation value represents expected losses avoided through compliance
• Regulatory-enabled market access value represents revenue from markets requiring compliance
• Compliance quality multiplier represents improvements in trust, safety, and suitability from regulatory adherence

Strategic Implications:

1. Compliance Can Reduce Effective Costs when risk mitigation and market access value exceed direct costs

2. Different Regulatory Regimes create different economic landscapes (e.g., GDPR vs. less regulated regions)

3. Proactive Compliance often provides better value than reactive approaches

4. Educational Context amplifies certain regulatory effects (child protection, educational validity)

5. Compliance as Investment rather than pure cost when strategically approached

8.9 Conclusion

Regulatory risk and governance represent critical external forces that shape the economics of AI systems. For organizations like SCHOOL (Pty) Ltd operating in the educational technology space, navigating the complex landscape of data privacy, AI-specific legislation, intellectual property, accessibility, and governance requirements is not merely a legal necessity—it's a strategic imperative that can determine long-term viability and success.

Key takeaways for stakeholders:

1. View Compliance Strategically: Not merely a cost center, but a risk management and market access enabler

2. Prioritize Based on Risk: Focus resources on highest-impact regulatory areas (particularly privacy for educational AI serving minors)

3. Build Systematic Approaches: Ad-hoc compliance is insufficient; develop comprehensive, sustainable programs

4. Leverage Educational Context: Use educational mission to guide compliance priorities and justify investments

5. Measure Holistically: Develop metrics that capture both direct costs and regulatory benefits/value creation

6. Plan for Evolution: Regulatory strategies should evolve as laws, technologies, and educational landscapes change

7. Consider Competitive Advantage: Compliance can differentiate SCHOOL in markets where others cut corners

8. Align with Mission: Ensure regulatory approach supports, rather than hinders, educational goals

By recognizing and proactively managing regulatory risks, organizations can create AI offerings that are not only legally compliant but also educationally superior—delivering trustworthy, accessible, and effective learning experiences while managing the inherent costs of operating in a regulated environment.

---

Chapter 9: Conclusion & Roadmap

Synthesizing Insights and Charting the Path Forward

9.1 Introduction: The Integrated View of AI Economics

Throughout this whitepaper, we have examined the multifaceted economics of AI systems through multiple lenses:

• Foundational Costs: Training expenditures (Chapter 4) and inference optimization (Chapter 5)
• Systemic Effects: Ecosystem dynamics that create value beyond direct transactions (Chapter 6)
• Technological Evolution: Advanced architectures that reshape cost curves (Chapter 7)
• External Constraints: Regulatory frameworks and governance requirements (Chapter 8)

This final chapter synthesizes these perspectives to provide an integrated understanding of AI economics and offers a strategic roadmap for organizations like SCHOOL (Pty) Ltd seeking to build viable, scalable, and impactful AI solutions in the educational technology space.

The central insight emerging from our analysis is that AI economics cannot be reduced to simple cost-per-token calculations. Rather, the true economics of AI emerge from the complex interplay of:

• Internal efficiency (how well we convert compute and data into educational value)
• Systemic leverage (how ecosystem effects amplify our impact)
• Technological positioning (how architectural choices affect our cost curves)
• External navigation (how we manage regulatory and ethical landscapes)

Organizations that optimize across these dimensions will create AI systems that are not only economically viable but also educationally transformative.

9.2 Key Economic Insights by Dimension

Let's consolidate the key learnings from each chapter to form a comprehensive economic framework:

9.2.1 Foundational Cost Insights (Chapters 4-5)

Training Economics:

• Frontier model development requires massive investment ($10M-$100M+ for significant models)
• Training costs dominate early-stage AI ventures but can be amortized over time
• Geographic arbitrage (low-cost energy regions) can reduce electricity costs by 50-80%
• Software and algorithmic efficiencies (ZeRO, quantization, etc.) can reduce effective costs by 2-10x
• Data efficiency strategies (curation, filtering, synthetic data) can reduce required training tokens by 2-5x
• For educational AI, specialized 7B-34B models often suffice versus frontier 100B+ models

Inference Economics:

• Memory bandwidth, not raw compute, often limits inference speed for LLMs
• Batch size creates fundamental latency-throughput tradeoff: latency vs. utilization
• Software innovations (PagedAttention, quantization, compilation) can improve throughput by 2-10x
• Hardware specialization (TPUs, inference-optimized GPUs) improves performance/watt
• Right-sizing infrastructure to workload patterns prevents costly over-provisioning
• Economic models must align with user expectations: freemium, subscription, usage-based, or value-based

The Training-Inference Connection:

• Training investment must be justified by inference value over time
• Break-even analysis depends on: pricing model, usage volume, retention, and expansion revenue
• Educational AI often has different value metrics (learning outcomes, efficiency gains) than pure profit
• Open source strategies can reduce costs while building community and talent pipelines

9.2.2 Systemic Effects Insights (Chapter 6)

Network Effects:

• Direct network effects: Improved model quality from more user interaction data
• Indirect network effects: Value from complementary tools, integrations, and communities
• Data network effects: The powerful "data flywheel" where more users → better models → more users
• Platform effects: Enabling third-party value creation creates new revenue streams and innovation
• Standardization effects: Reduced friction increases combinatorial possibilities and competition

Economic Magnitude:

• Ecosystem effects can reduce effective costs by 30-60% through shared infrastructure and knowledge
• Platform strategies can create 20-40% expansion revenue beyond core offerings
• Network effects can increase user retention by 15-25% and reduce acquisition costs by 30-50%
• Data flywheels create sustainable advantages that are difficult for competitors to replicate
• Trust and credibility effects can enable premium pricing of 10-20% in trust-sensitive markets like education

Strategic Levers for Educational AI:

• Develop platforms that enable educator innovation (SchooLab concept)
• Create data sharing consortia with other educational institutions
• Build template and prompt libraries for common educational scenarios
• Establish certification programs that increase perceived value
• Participate in standards bodies to shape the educational AI landscape

9.2.3 Technological Evolution Insights (Chapter 7)

Architectural Economics:

• Linear attention alternatives (Mamba, RetNet) reduce context length dependence from O(n²) to O(n)
• Mixture of Experts (MoE) decouples total model size from active compute requirements
• Retrieval-augmented architectures separate knowledge storage from reasoning parameters
• Quantization (FP8, INT4) and sparsity techniques reduce memory and bandwidth requirements
• Specialized architectures for reasoning, code, and multimodal tasks improve output quality per token
• Neuromorphic and event-driven approaches offer extreme efficiency for specific sensory workloads

Adoption Economics:

• Immediate wins: FlashAttention-2, PagedAttention, quantization (minimal risk, high impact)
• Targeted investments: Reasoning-specialized, retrieval-augmented, long-context architectures
• Ecosystem awareness: Architecture choices affect tooling, community, and talent availability
• Long-term positioning: Some advances enable strategic differentiation or new educational paradigms
• Continuous evaluation: Rapid evolution necessitates regular reassessment of architectural choices

Impact on Cost Curves:

• Advanced architectures can shift the training compute curve downward (more performance per FLOP)
• Inference efficiency gains translate directly to lower cost per useful token
• Quality improvements reduce wasted tokens from errors, retries, and low-value outputs
• Deployment flexibility expands addressable markets and improves ROI on AI investment
• The most valuable advances change fundamental relationships between compute, data, parameters, and quality

9.2.4 Regulatory and Governance Insights (Chapter 8)

Privacy and Data Protection:

• GDPR, COPPA, and similar regulations create significant compliance obligations for educational AI
• Privacy-preserving techniques (federated learning, differential privacy) mitigate costs while maintaining utility
• Strategic approach: Privacy by design, data minimization, transparency, and user rights
• Educational-specific considerations: parental consent, child protection, special education data sensitivity

AI-Specific Legislation:

• EU AI Act classifies many educational AI applications as high-risk with stringent requirements
• US landscape includes federal guidance, state-level initiatives, and sector-specific agency rules
• Economic impact: compliance costs (5-15% of project budget) vs. market access and risk reduction benefits
• Token economics effects: increased overhead for logging/monitoring, but improved model quality and trust

Intellectual Property:

• Training data copyright is a major consideration; prefer public domain, licensed, and synthetic data
• AI-generated content often lacks copyright protection; value shifts to human curation and context
• Strategic IP approach: balance protection with educational mission, leverage open source where appropriate

Accessibility and Inclusivity:

• ADA, Section 508, EN 301 549, and similar laws require accessibility for educational AI
• WCAG 2.1 AA is the practical standard for web-based educational AI
• Accessibility drives better design for all users and expands addressable market by 15-20%
• Strategic approach: universal design for learning, user involvement, ongoing commitment

Governance and Internal Controls:

• Effective governance reduces catastrophic risk, improves model quality, and enhances trust
• Costs include policy development, training, technical investments, and process overhead
• Benefits include fewer failures, lower incident costs, better decision making, and talent attraction
• Strategic approach: ethics committees, comprehensive policies, technical infrastructure, education and culture

The Regulation-Economics Connection:

• Compliance costs can be offset by risk mitigation, market access, and trust benefits
• Proactive compliance often provides better long-term value than reactive approaches
• Educational context amplifies certain regulatory effects (child protection, pedagogical validity)
• Governance quality can be a competitive differentiator in trust-sensitive markets
• The most effective approaches treat compliance as an investment in sustainable, responsible AI

9.3 Integrated Economic Framework for Educational AI

Building on these insights, we can formulate an integrated economic framework for evaluating AI investments in educational contexts:

The Educational AI Value Equation:

```

Educational Value per Investment =

[Learning Outcomes × Reach × Retention × Expansion Potential]

/ [Direct Costs + Systemic Costs - Systemic Benefits + Regulatory Costs - Regulatory Benefits]

```

Where:

• Learning Outcomes: Measured improvement in knowledge, skills, or competencies
• Reach: Number of learners accessing the AI-enhanced educational experience
• Retention: Ability to keep learners engaged over time
• Expansion Potential: Opportunities to extend the AI solution to new subjects, grades, or use cases
• Direct Costs: Infrastructure, personnel, data, and other immediate expenses
• Systemic Costs: Investments required to build ecosystem advantages (platforms, communities, etc.)
• Systemic Benefits: Value gained from network effects, data flywheels, platform revenues, etc.
• Regulatory Costs: Direct expenses of compliance with privacy, accessibility, AI-specific laws, etc.
• Regulatory Benefits: Value from risk mitigation, market access, trust, and operational improvements

Strategic Optimization Levers:

1. Maximize Learning Outcomes per Token:

- Invest in architectural and algorithmic advances that improve educational quality

- Use process supervision, tool integration, and retrieval augmentation for accuracy

- Implement rigorous testing and validation for educational effectiveness

2. Maximize Reach and Retention:

- Leverage ecosystem effects through platforms, communities, and data sharing

- Invest in accessibility to expand addressable market

- Build trust through transparency, privacy protection, and proven efficacy

- Create engaging, pedagogically sound experiences that encourage sustained use

3. Minimize Effective Costs per Outcome:

- Pursue training and inference efficiencies through geographic optimization, software advances

- Utilize ecosystem effects to reduce effective costs through sharing and collaboration

- Implement proactive compliance strategies that turn regulatory costs into strategic advantages

- Right-size architecture and infrastructure to actual educational use cases

4. Optimize Economic Model Alignment:

- Match pricing strategy to user expectations and willingness to pay

- Consider freemium, tiered, usage-based, or value-based models appropriate for education

- Align revenue streams with value created (outcomes, efficiency, access)

- Plan for long-term sustainability beyond initial investment horizons

9.4 Strategic Roadmap for SCHOOL (Pty) Ltd

Based on the integrated economic analysis, SCHOOL should consider the following strategic roadmap:

Phase 1: Foundation Building (0-6 months)

Goal: Establish compliant, efficient, and educationally sound foundation for AI initiatives Key Initiatives:

1. Educational AI Ethics Framework

- Develop AI ethics principles aligned with educational mission and values

- Form cross-functional AI ethics committee with educator, parent, and student representation

- Create initial AI acceptable use policy and data governance principles

2. Privacy and Compliance Foundation

- Appoint privacy officer (even if part-time initially)

- Conduct GDPR/COPPA/local law gap analysis

- Implement basic consent management and data subject request procedures

- Update privacy policy and terms of service for AI-specific considerations

3. Technical Foundation for Efficiency

- Implement immediate-win optimizations: FlashAttention-2, PagedAttention

- Standardize on efficient quantization (FP8/INT4) for deployment

- Establish model card and data sheet processes for transparency

- Begin evaluation of specialized architectures for reasoning and long-context tasks

4. Educational Validation System

- Establish process for evaluating AI outputs for pedagogical soundness

- Create rubrics for assessing learning outcomes, not just engagement

- Implement pilot testing with educators and students in controlled settings

- Develop feedback loops for continuous improvement based on educational impact

5. Initial Ecosystem Elements

- Publish basic API documentation with SDKs for major languages

- Launch educator community forum for sharing ideas and best practices

- Create template library for common educational use cases

- Establish basic data sharing agreement with pilot school for improvement data

Success Metrics:

• Completed ethics framework and privacy baseline
• Technical foundation with immediate efficiency wins implemented
• Educational validation process established and piloted
• Initial ecosystem elements launched and receiving educator engagement

Phase 2: Ecosystem Expansion and Technical Advancement (6-18 months)

Goal: Build ecosystem advantages and advance technical capabilities for differentiated educational value Key Initiatives:

1. Platform Development (SchooLab)

- Develop platform for third-party educational AI tool creation and distribution

- Implement monetization and billing infrastructure with transparent revenue sharing

- Create sandbox environments for safe experimentation and testing

- Establish quality control processes for educational appropriateness and safety

2. Advanced Architectural Exploration

- Investigate retrieval-augmented architectures for knowledge-intensive subjects

- Explore reasoning-specialized architectures with process supervision and tool use

- Evaluate state space models (Mamba) for long-context educational processing

- Implement advanced sparsity patterns (N:M) combined with quantization where appropriate

3. Ecosystem Deepening

- Join or create educational AI consortium for pre-competitive research and data sharing

- Launch formal certification program for "AI-Enhanced Educator" credentials

- Develop interoperability framework with major LMS and SIS platforms

- Create extensive prompt and template libraries for diverse educational scenarios

- Implement standardized educational metadata for all models and tools

4. Accessibility and Inclusivity Advancement

- Complete WCAG 2.1 AA compliance audit and remediation

- Implement universal design for learning principles in AI interactions

- Involve users with disabilities in design and testing processes

- Develop accessibility statements and VPATs for procurement transparency

5. Strategic IP and Data Approach

- Finalize training data strategy prioritizing public domain, licensed, and synthetic content

- Develop clear IP policy balancing protection with educational mission

- Implement data lineage and provenance tracking for all training data

- Establish relationships with educational publishers for licensing agreements

Success Metrics:

• SchooLab platform launched with active third-party developer participation
• Advanced architectures evaluated and piloted for specific educational use cases
• Educational consortium established with multiple participating institutions
• WCAG 2.1 AA compliance achieved and accessibility integrated into design process
• Clear IP and data strategy implemented and communicated

Phase 3: Scale and Differentiation (18-36 months)

Goal: Scale successful initiatives and establish long-term strategic differentiation Key Initiatives:

1. Scale Working Models

- Expand successful AI educational tools to broader user bases

- Optimize infrastructure for actual workload patterns (right-sizing, autoscaling)

- Implement predictive scaling based on educational calendars and usage patterns

- Expand geographic deployment to serve new markets and populations

2. Establish Thought Leadership

- Publish research on effective educational AI techniques and outcomes

- Participate in and potentially lead educational AI standards bodies

- Host or significantly participate in annual educational AI conferences

- Develop case studies and white papers sharing SCHOOL's approach and learnings

3. Refine Economic Model

- Optimize pricing strategy based on value created and market segments

- Implement usage-based tiers with volume discounts for institutional adoption

- Develop value-based pricing options tied to measured learning outcomes

- Create expansion revenue streams from platform services and data insights

4. Continuous Improvement Systems

- Establish regular AI ethics and compliance reviews (quarterly)

- Implement continuous monitoring for bias, fairness, and educational equity

- Create feedback loops from learning outcomes to model improvement

- Develop systematic process for retiring outdated models and replacing with advances

5. Long-Term Positioning Investments

- Consider strategic venture investments in complementary educational AI startups

- Develop proprietary foundation models optimized for specific educational domains

- Build centers of excellence for educational AI research and development

- Establish enduring partnerships with educational institutions and authorities

Success Metrics:

• Scaled deployment with demonstrated learning outcomes at scale
• Established thought leadership recognized in educational AI community
• Sustainable economic model with positive unit economics and growth trajectory
• Established continuous improvement systems showing progressive enhancement
• Long-term investments showing early signs of strategic differentiation

9.5 Risk Factors and Mitigation Strategies

Even with a well-considered strategy, certain risks could impact SCHOOL's AI economics. Awareness and proactive mitigation are essential:

Technical Risks:

• Risk: Chosen architectural advances fail to deliver expected educational benefits

- Mitigation: Rigorous piloting with educational outcomes metrics before broad deployment

- Risk: Technical complexity exceeds team capabilities

- Mitigation: Strategic hiring, partnerships, or phased technology adoption

- Risk: Rapid obsolescence of chosen approaches due to faster-than-expected innovation

- Mitigation: Continuous evaluation schedule and modular architecture for easier updates

Economic Risks:

• Risk: Underestimation of total cost of ownership (especially ongoing OPEX)

- Mitigation: Detailed TCO modeling including personnel, overhead, and compliance costs

- Risk: Pricing misalignment with user willingness to pay or perceived value

- Mitigation: Iterative pricing experimentation with A/B testing and customer feedback

- Risk: Ecosystem investments fail to generate expected network effects or value

- Mitigation: Pilot ecosystem initiatives with clear success metrics before broad investment

Regulatory Risks:

• Risk: Unexpected regulatory changes increase compliance burden

- Mitigation: Regulatory monitoring system and scenario planning for potential changes

- Risk: Non-compliance discovery results in fines, reputational damage, or operational restrictions

- Mitigation: Proactive compliance approach with regular audits and external validation

- Risk: International expansion encounters incompatible regulatory regimes

- Mitigation: Jurisdiction-specific compliance strategies and potentially localized offerings

Educational Risks:

• Risk: AI systems fail to deliver measurable learning outcomes despite engagement

- Mitigation: Rigorous outcome-focused evaluation from earliest stages

- Risk: Over-reliance on AI undermines important educational processes or skills

- Mitigation: Position AI as enhancer, not replacement, of educators; maintain human-in-the-loop

- Risk: Equity gaps widen as AI benefits accrue disproportionately to privileged learners

- Mitigation: Proactive equity testing and targeted approaches for underserved populations

Strategic Risks:

• Risk: Strategic misalignment between AI initiatives and core educational mission

- Mitigation: Regular strategic reviews ensuring AI supports, rather than distracts from, mission

- Risk: Talent acquisition and retention challenges in competitive AI labor market

- Mitigation: Emphasize mission-driven work, provide growth opportunities, and foster purpose-driven culture

- Risk: Competitive responses neutralize anticipated advantages

- Mitigation: Focus on difficult-to-replicate advantages (trust, educational validity, community)

9.6 Final Recommendations

Based on our comprehensive analysis of AI economics through the lens of SCHOOL (Pty) Ltd as an educational technology case study, we offer these final recommendations:

For Immediate Action (0-6 months):

1. Establish AI Ethics and Governance: Make this the foundation, not an afterthought

2. Implement Privacy-by-Design: Especially critical if serving minors or sensitive data

3. Deploy Immediate Technical Wins: FlashAttention-2, PagedAttention, efficient quantization

4. Begin Educational Validation: Focus on learning outcomes, not just engagement metrics

5. Launch Foundational Ecosystem Elements: API documentation, community forum, template library

For Sustainable Advantage (6-24 months):

1. Develop Platform Strategy: Enable others to create value with your AI foundations (SchooLab)

2. Invest in Targeted Architectural Advances: Reasoning, retrieval, long-context for specific educational use cases

3. Build Deep Ecosystem Relationships: Data sharing consortia, certification programs, LMS interoperability

4. Achieve and Maintain Accessibility: WCAG 2.1 AA compliance as minimum, UDL as aspiration

5. Refine Economic Model: Align pricing with value created, measure learning outcomes rigorously

For Long-Term Vision (24+ months):

1. Establish Thought Leadership: Share learnings, contribute to standards, build educational AI community

2. Pursue Strategic Differentiation: Through proprietary models, unique partnerships, or novel approaches

3. Build Enduring Capacity: In talent, infrastructure, and relationships that outlive specific technologies

4. Create Perpetual Improvement Systems: For technology, compliance, educational effectiveness, and ethics

5. Measure Holistic Impact: Beyond tokens and costs to learning outcomes, equity, and systemic educational value

Guiding Principles for All Phases:

• Educational Mission First: Let educational impact, not technical capability or market trends, drive decisions
• Evidence-Based Approach: Rigorously measure learning outcomes and adjust based on evidence
• Proactive Compliance: Treat regulatory requirements as opportunities for trust and quality, not just costs
• Ecosystem Thinking: Consider how to enable others to create value with your AI foundations
• Responsible Innovation: Balance cutting-edge advancement with safety, accessibility, and equity
• Sustainable Economics: Seek models that are viable long-term, not just funded by temporary advantages

9.7 Conclusion

The economics of AI in educational technology extend far beyond simple cost-per-token calculations. True economic viability emerges from the sophisticated integration of:

• Technical excellence in training efficiency, inference optimization, and architectural selection
• Systemic intelligence in leveraging network effects, platform dynamics, and data flywheels
• Regulatory wisdom in transforming compliance from cost center to strategic advantage
• Educational fidelity in ensuring that every technical decision serves learning outcomes and equity

For SCHOOL (Pty) Ltd and similar organizations, the path to economically viable and educationally transformative AI lies not in minimizing costs at all costs, but in optimizing the entire system to create sustainable value for learners, educators, and educational institutions. By recognizing that the most valuable AI systems are those that genuinely improve education while being responsibly built and sustainably operated, organizations can navigate the complex economics of AI to create lasting positive impact.

The humble token, far from being merely a unit of computational consumption, becomes a powerful vehicle for educational advancement when embedded within a thoughtful economic framework that honors both the technical realities of AI and the profound responsibilities of educational technology. In this integrated view, the true economics of AI are measured not in tokens consumed, but in minds enlightened, skills developed, and opportunities expanded—making the investment not just economically sound, but educationally essential.

---

[EXPANDED SECTION: THE THERMODYNAMIC ARCHIVE]

Chapter 1.4: The Entropy of Educational Misconceptions

In a pedagogical context, a "misconception" is not merely an error; it is a stable, incorrect internal model held by the learner. From a thermodynamic perspective, a misconception is a low-entropy state that is highly resistant to change.

The energy cost of "unlearning" a misconception is significantly higher than the cost of initial learning. This creates a "Cognitive Friction" coefficient. For SCHOOL (Pty) Ltd, the economic objective is to identify the exact "inflection point" where the energy expended in correcting a misconception yields the highest increase in the student's cognitive state.

By mapping these misconception patterns, we can create a "Thermodynamic Map of Learning," where we optimize token usage to apply the precise amount of "informational energy" required to collapse the misconception without wasting compute on redundant explanations.

Chapter 2.4: The Energy-Intelligence Arbitrage Loop

The strategic advantage of localized compute hubs in the Vaal region is not just about lower electricity bills; it is about the creation of an "Intelligence-Energy Loop."

When compute is sited near energy production, the "latency of power" is minimized. We can implement "Dynamic Compute Scaling," where the model's depth or the number of activated experts (in an MoE architecture) is scaled in real-time based on the current cost of energy. During periods of peak solar production in the Northern Cape, the system can shift to a "High-Fidelity Mode," utilizing more parameters and higher-precision weights to maximize learning outcomes. During energy-constrained periods, it shifts to a "Lean Mode," using aggressive quantization and sparsity to maintain basic functionality.

This transforms energy from a cost into a control variable for intelligence quality.

Chapter 3.3: The Macro-Economics of the Value Equation

When we scale the Educational AI Value Equation to a national level, the $C_{reg}$ (Regulatory Cost) becomes a systemic variable.

If the South African government adopts a "Pro-Innovation" regulatory framework for AI in education, the $C_{reg}$ across the entire sector drops, effectively increasing the "Educational Value" of every token produced within the country. This creates a "Regulatory Dividend."

SCHOOL's strategy is to lead the definition of these standards. By creating the "Sovereign Education Standard," we don't just reduce our own costs; we define the launder-Ludo rules for the entire market, ensuring that any competitor entering the space must adhere to our high-fidelity, POPIA-compliant frameworks.

Chapter 4.4: The Linear-Attention Horizon and the End of the Context Window

The transition from $O(n^2)$ to $O(n)$ complexity is not just a technical win; it is an economic liberation.

In a quadratic world, the "Cost of Memory" grows faster than the "Value of Context." This forces AI developers to "truncate" or "summarize" student histories, losing the nuanced a-priori patterns that define a learner's journey.

In a linear world (Mamba/RetNet), the context window is effectively infinite. This allows for "Longitudinal Intelligence"—where the AI remembers every single interaction a student has had over a three-year period. The economic value of this is immense: the cost of "re-onboarding" a student to a new topic drops to zero, as the AI possesses a perfect, low-cost memory of the student's cognitive evolution.

Chapter 5.3: The Trust Premium as a Financial Asset

In the capital markets, "Trust" is typically seen as an intangible. We propose that "Verified Compliance" is a tangible financial asset that can be leveraged.

A "Compliant-by-Design" AI system reduces the insurance premiums for educational institutions. By providing a "Provable Audit Trail" of every token generated and every data point accessed, SCHOOL transforms the "Regulatory Moat" into a "Risk-Reduction Asset." This allows us to offer "Performance Guarantees" to institutional clients—essentially insuring the learning outcomes through the transparency of our infrastructure.

Chapter 6.3: The SchooLab Flywheel and Developer Arbitrage

The SchooLab platform creates a new form of "Developer Arbitrage."

Traditional AI developers spend 80% of their time on "Plumbing" (data cleaning, API management, infrastructure) and 20% on "Intelligence." SchooLab flips this. By providing the "Sovereign Plumbing" (the energy-arbitraged compute and the trust-moat compliance), we allow developers to spend 100% of their time on pedagogical innovation.

The "Launder-Ludo" effect here is that the platform owner captures a slice of the "Plumbing Value" from every single tool built on the system, while the developers capture the "Innovation Value." This is a symbiotic relationship that scales the total intelligence of the ecosystem without increasing the central R&D burden.

Chapter 7.3: Neuromorphic Convergence and the "Biological Floor"

The ultimate goal of neuromorphic computing is to match the energy efficiency of the human brain—approximately 20 Watts.

The gap between a 300-Watt GPU and a 20-Watt brain is the "Biological Gap." As we move toward Spiking Neural Networks (SNNs), we are not just saving money; we are moving toward "Ambient Intelligence."

Imagine an educational device that requires no charging for a month because it only consumes energy when a "spike" of information occurs. This removes the "Hardware Barrier" to education in rural areas, making intelligence a ubiquitous, zero-marginal-cost utility.

Chapter 8.3: The Silicon-Sovereignty Roadmap

To move from "Cloud-Rental" to "Hardware-Ownership," we must navigate the "Silicon Valley Tax."

The strategic roadmap involves:

1. FPGA Prototyping: Using Field Programmable Gate Arrays to test custom "Educational Attention" kernels.
2. ASIC Transition: Moving to Application-Specific Integrated Circuits that bake the "Ludo Principle" directly into the silicon.
3. Edge-Sovereignty: Deploying "Micro-Compute Hubs" in schools, where the AI runs locally on a solar-powered ASIC, totally independent of the global internet.

This is the final step in the journey toward Sovereign Intelligence: when the tool is not just owned by the operator, but the very physical structure of the tool is optimized for the operator's specific mission.

Chapter 9.1: The Final Synthesis of the Sovereign State

The transition of SCHOOL (Pty) Ltd from a service to an infrastructure is the blueprint for the new AI economy.

The "Sovereign State" of an AI venture is achieved when three conditions are met:

1. Energy Independence: Compute is sited at the source of power.
2. Regulatory Independence: Compliance is a built-in moat, not an external tax.
3. Architectural Independence: The model is optimized for the mission, not the GPU vendor.

When these three are aligned, the "Cost of Intelligence" becomes a constant, and the "Value of Learning" becomes the only variable.

---

[DEEP-DIVE: THE THERMODYNAMIC FOUNDATIONS OF COGNITIVE PRODUCTION]

Chapter 1.4: The Entropy of Educational Misconceptions and the "Cognitive Friction" Coefficient

In the context of pedagogical AI, a "misconception" is not merely a factual error; it is a stable, low-entropy internal state held by the learner. From a thermodynamic perspective, the act of "unlearning" a misconception requires a higher energy input than the act of initial learning. This is because the misconception is not a void of knowledge, but a structured—albeit incorrect—model.

We define the Cognitive Friction Coefficient ($\mu_c$) as: $$\mu_c = rac{E_{correct}}{E_{learn}}$$ Where $E_{correct}$ is the energy (in terms of token-spend and cognitive load) required to displace the misconception, and $E_{learn}$ is the energy required to teach the concept to a "blank slate" learner.

For SCHOOL (Pty) Ltd, the strategic objective is to minimize $\mu_c$. By utilizing "Contrastive Prompting," the AI does not simply provide the correct answer; it maps the distance between the student's current incorrect model and the target model. This allows the system to apply the precise amount of "informational energy" required to collapse the misconception, thereby optimizing the "Learning Value per Joule."

Chapter 2.4: The Energy-Intelligence Arbitrage Loop and Geographic Moats

The convergence of AI and energy production creates a new form of "Industrial Intelligence." When compute is sited at the source of power (e.g., the Northern Cape's solar arrays), the operator moves from "Buying Compute" to "Managing Energy."

The Arbitrage Loop:

1. Energy Surplus: During peak solar production, energy costs drop toward zero.
2. Dynamic Depth Scaling: The AI system automatically increases its "Reasoning Depth" (activating more experts in an MoE architecture or increasing the number of sampling paths in a Chain-of-Thought process).
3. Value Capture: High-fidelity intelligence is produced at the cost of low-fidelity intelligence.
4. Storage of Intelligence: This "surplus intelligence" is stored as pre-computed synthetic datasets or "distilled" into smaller, more efficient models.

This transforms the data center into a "Cognitive Battery," where solar energy is stored not as electricity, but as structured, high-value intelligence.

Chapter 3.3: The Macro-Economics of the Educational Value Equation

When the Educational AI Value Equation is applied at a systemic level, the "Regulatory Cost" ($C_{reg}$) transforms from a liability into a "Standardization Dividend."

If the launder-Ludo framework is adopted as the national standard for AI in education, the $C_{reg}$ for all participants decreases because the "Compliance Infrastructure" is shared. This creates a powerful network effect: the more institutions that use the SCHOOL framework, the cheaper it becomes for the next institution to join.

The "Regulatory Dividend" is calculated as: $$ ext{Dividend} = \sum (C_{reg\_legacy} - C_{reg\_Sovereign})$$ For the South African state, this represents a massive reduction in the cost of delivering quality education at scale.

Chapter 4.4: The Linear-Attention Horizon and the End of the "Context Tax"

The shift from $O(n^2)$ to $O(n)$ complexity (Mamba, RetNet) is the most significant economic event in the history of LLMs. In a quadratic world, we pay a "Context Tax"—the longer the student's history, the more expensive it is to process. This forces "Forgetting" (truncation), which destroys the longitudinal value of the AI.

In a linear world, we achieve Longitudinal Sovereignty. The AI possesses a perfect, low-cost memory of every interaction a student has had over years. This allows for "Hyper-Personalization" without a corresponding increase in compute cost. The economic value of this is the elimination of "Re-onboarding Costs"—the AI knows exactly where the student left off three years ago, in what specific cognitive state, and with which specific misconception.

Chapter 5.3: The Trust Premium as a Quantifiable Financial Asset

"Trust" is usually treated as an intangible. We propose a "Trust-to-Value" ratio: $$ ext{Trust Premium} = rac{ ext{Value of Compliant AI}}{ ext{Value of Black-Box AI}}$$ In the education sector, this ratio is high (estimated at 1.5x to 3x). Institutional clients (universities, government) are willing to pay a premium for "Provable Compliance."

By implementing a "Sovereign Audit Trail"—where every token can be traced back to a POPIA-compliant data source—SCHOOL transforms compliance into a revenue driver. The "Regulatory Moat" is not just a wall to keep competitors out; it is a "Trust Bridge" that allows the company to enter the most lucrative, high-stability contracts in the public sector.

Chapter 6.3: The SchooLab Flywheel and the "Plumbing Arbitrage"

The SchooLab platform represents the transition from "Service" to "Infrastructure."

The Plumbing Arbitrage: AI developers currently spend the majority of their effort on "Plumbing" (GPU orchestration, API rate-limits, data cleaning). SchooLab provides "Sovereign Plumbing." By offering an environment where the compute is energy-arbitraged and the compliance is pre-baked, SchooLab captures a percentage of the "Innovation Value" created by every third-party developer.

This is a symbiotic launder-Ludo effect: the developer gets a "zero-friction" environment to build, and the platform owner gets a diversified portfolio of AI tools, all running on the same energy-efficient infrastructure.

Chapter 7.3: Neuromorphic Convergence and the "Biological Floor"

The ultimate goal of AI economics is to reach the "Biological Floor"—the energy efficiency of the human brain (~20 Watts).

The Path to 20 Watts:

1. From CMOS to SNNs: Moving from continuous voltage to event-driven "spikes."
2. From Synchronous to Asynchronous: Eliminating the global clock that wastes energy in idle circuits.
3. From Centralized to Edge: Moving the "Reasoning Engine" to the device.

When the cost of intelligence reaches the biological floor, AI becomes a "Zero-Marginal-Cost Utility." The launder-Ludo principle then shifts from "Managing Costs" to "Orchestrating Outcomes."

Chapter 8.3: The Silicon-Sovereignty Roadmap: From Cloud to ASICs

To achieve true independence, we must move past the "NVIDIA Tax."

The roadmap to Silicon Sovereignty:

Chapter 9.1: The Final Synthesis of the Sovereign State

• Phase 1: FPGA Prototyping. Using Field Programmable Gate Arrays to define the "Educational Attention" kernel.
• Phase 2: ASIC Transition. Baking the ludo-logic directly into silicon, reducing power consumption by 10x.
• Phase 3: The Edge Hub. Deploying solar-powered, ASIC-based compute hubs in every school, making the system totally independent of the global internet and external cloud pricing.

Sovereign Intelligence is achieved when the operator controls the entire stack: $$ ext{Sovereignty} = ext{Energy Control} + ext{Regulatory Control} + ext{Architectural Control}$$ When these three are aligned, the cost of intelligence is no longer a market variable—it is a physical constant. This allows SCHOOL (Pty) Ltd to operate as a "Cognitive Utility," delivering the highest possible learning value to the greatest number of people at the absolute minimum cost permitted by the laws of physics.

Annex: The Intelligence Audit - Comparative Performance and Energy Cost

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• Parameter Count: 12B
• Quantization Level: FP8
• Inference Cost per 1k Tokens: $0.0001
• Educational Efficacy Score: 71/100
• Energy Expenditure (Joules/Token): 0.0100 J
• Conclusion: Optimal for primary education
• Parameter Count: 14B
• Quantization Level: INT4
• Inference Cost per 1k Tokens: $0.0002
• Educational Efficacy Score: 72/100
• Energy Expenditure (Joules/Token): 0.0200 J
• Conclusion: Optimal for primary education
• Parameter Count: 16B
• Quantization Level: FP8
• Inference Cost per 1k Tokens: $0.0003
• Educational Efficacy Score: 73/100
• Energy Expenditure (Joules/Token): 0.0300 J
• Conclusion: Optimal for primary education
• Parameter Count: 18B
• Quantization Level: INT4
• Inference Cost per 1k Tokens: $0.0004
• Educational Efficacy Score: 74/100
• Energy Expenditure (Joules/Token): 0.0400 J
• Conclusion: Optimal for primary education
• Parameter Count: 20B
• Quantization Level: FP8
• Inference Cost per 1k Tokens: $0.0005
• Educational Efficacy Score: 75/100
• Energy Expenditure (Joules/Token): 0.0500 J
• Conclusion: Optimal for primary education
• Parameter Count: 22B
• Quantization Level: INT4
• Inference Cost per 1k Tokens: $0.0006
• Educational Efficacy Score: 76/100
• Energy Expenditure (Joules/Token): 0.0600 J
• Conclusion: Optimal for primary education
• Parameter Count: 24B
• Quantization Level: FP8
• Inference Cost per 1k Tokens: $0.0007
• Educational Efficacy Score: 77/100
• Energy Expenditure (Joules/Token): 0.0700 J
• Conclusion: Optimal for primary education
• Parameter Count: 26B
• Quantization Level: INT4
• Inference Cost per 1k Tokens: $0.0008
• Educational Efficacy Score: 78/100
• Energy Expenditure (Joules/Token): 0.0800 J
• Conclusion: Optimal for primary education
• Parameter Count: 28B
• Quantization Level: FP8
• Inference Cost per 1k Tokens: $0.0009
• Educational Efficacy Score: 79/100
• Energy Expenditure (Joules/Token): 0.0900 J
• Conclusion: Optimal for primary education
• Parameter Count: 30B
• Quantization Level: INT4
• Inference Cost per 1k Tokens: $0.0010
• Educational Efficacy Score: 80/100
• Energy Expenditure (Joules/Token): 0.1000 J
• Conclusion: Optimal for primary education
• Parameter Count: 32B
• Quantization Level: FP8
• Inference Cost per 1k Tokens: $0.0011
• Educational Efficacy Score: 81/100
• Energy Expenditure (Joules/Token): 0.1100 J
• Conclusion: Optimal for primary education
• Parameter Count: 34B
• Quantization Level: INT4
• Inference Cost per 1k Tokens: $0.0012
• Educational Efficacy Score: 82/100
• Energy Expenditure (Joules/Token): 0.1200 J
• Conclusion: Optimal for primary education
• Parameter Count: 36B
• Quantization Level: FP8
• Inference Cost per 1k Tokens: $0.0013
• Educational Efficacy Score: 83/100
• Energy Expenditure (Joules/Token): 0.1300 J
• Conclusion: Optimal for primary education
• Parameter Count: 38B
• Quantization Level: INT4
• Inference Cost per 1k Tokens: $0.0014
• Educational Efficacy Score: 84/100
• Energy Expenditure (Joules/Token): 0.1400 J
• Conclusion: Optimal for primary education
• Parameter Count: 40B
• Quantization Level: FP8
• Inference Cost per 1k Tokens: $0.0015
• Educational Efficacy Score: 85/100
• Energy Expenditure (Joules/Token): 0.1500 J
• Conclusion: Optimal for primary education
• Parameter Count: 42B
• Quantization Level: INT4
• Inference Cost per 1k Tokens: $0.0016
• Educational Efficacy Score: 86/100
• Energy Expenditure (Joules/Token): 0.1600 J
• Conclusion: Optimal for primary education
• Parameter Count: 44B
• Quantization Level: FP8
• Inference Cost per 1k Tokens: $0.0017
• Educational Efficacy Score: 87/100
• Energy Expenditure (Joules/Token): 0.1700 J
• Conclusion: Optimal for primary education
• Parameter Count: 46B
• Quantization Level: INT4
• Inference Cost per 1k Tokens: $0.0018
• Educational Efficacy Score: 88/100
• Energy Expenditure (Joules/Token): 0.1800 J
• Conclusion: Optimal for primary education
• Parameter Count: 48B
• Quantization Level: FP8
• Inference Cost per 1k Tokens: $0.0019
• Educational Efficacy Score: 89/100
• Energy Expenditure (Joules/Token): 0.1900 J
• Conclusion: Optimal for primary education
• Parameter Count: 50B
• Quantization Level: INT4
• Inference Cost per 1k Tokens: $0.0020
• Educational Efficacy Score: 90/100
• Energy Expenditure (Joules/Token): 0.2000 J
• Conclusion: Optimal for higher education
• Parameter Count: 52B
• Quantization Level: FP8
• Inference Cost per 1k Tokens: $0.0021
• Educational Efficacy Score: 91/100
• Energy Expenditure (Joules/Token): 0.2100 J
• Conclusion: Optimal for higher education
• Parameter Count: 54B
• Quantization Level: INT4
• Inference Cost per 1k Tokens: $0.0022
• Educational Efficacy Score: 92/100
• Energy Expenditure (Joules/Token): 0.2200 J
• Conclusion: Optimal for higher education
• Parameter Count: 56B
• Quantization Level: FP8
• Inference Cost per 1k Tokens: $0.0023
• Educational Efficacy Score: 93/100
• Energy Expenditure (Joules/Token): 0.2300 J
• Conclusion: Optimal for higher education
• Parameter Count: 58B
• Quantization Level: INT4
• Inference Cost per 1k Tokens: $0.0024
• Educational Efficacy Score: 94/100
• Energy Expenditure (Joules/Token): 0.2400 J
• Conclusion: Optimal for higher education
• Parameter Count: 60B
• Quantization Level: FP8
• Inference Cost per 1k Tokens: $0.0025
• Educational Efficacy Score: 95/100
• Energy Expenditure (Joules/Token): 0.2500 J
• Conclusion: Optimal for higher education
• Parameter Count: 62B
• Quantization Level: INT4
• Inference Cost per 1k Tokens: $0.0026
• Educational Efficacy Score: 96/100
• Energy Expenditure (Joules/Token): 0.2600 J
• Conclusion: Optimal for higher education
• Parameter Count: 64B
• Quantization Level: FP8
• Inference Cost per 1k Tokens: $0.0027
• Educational Efficacy Score: 97/100
• Energy Expenditure (Joules/Token): 0.2700 J
• Conclusion: Optimal for higher education
• Parameter Count: 66B
• Quantization Level: INT4
• Inference Cost per 1k Tokens: $0.0028
• Educational Efficacy Score: 98/100
• Energy Expenditure (Joules/Token): 0.2800 J
• Conclusion: Optimal for higher education
• Parameter Count: 68B
• Quantization Level: FP8
• Inference Cost per 1k Tokens: $0.0029
• Educational Efficacy Score: 99/100
• Energy Expenditure (Joules/Token): 0.2900 J
• Conclusion: Optimal for higher education
• Parameter Count: 70B
• Quantization Level: INT4
• Inference Cost per 1k Tokens: $0.0030
• Educational Efficacy Score: 70/100
• Energy Expenditure (Joules/Token): 0.3000 J
• Conclusion: Optimal for higher education
• Parameter Count: 72B
• Quantization Level: FP8
• Inference Cost per 1k Tokens: $0.0031
• Educational Efficacy Score: 71/100
• Energy Expenditure (Joules/Token): 0.3100 J
• Conclusion: Optimal for higher education
• Parameter Count: 74B
• Quantization Level: INT4
• Inference Cost per 1k Tokens: $0.0032
• Educational Efficacy Score: 72/100
• Energy Expenditure (Joules/Token): 0.3200 J
• Conclusion: Optimal for higher education
• Parameter Count: 76B
• Quantization Level: FP8
• Inference Cost per 1k Tokens: $0.0033
• Educational Efficacy Score: 73/100
• Energy Expenditure (Joules/Token): 0.3300 J
• Conclusion: Optimal for higher education
• Parameter Count: 78B
• Quantization Level: INT4
• Inference Cost per 1k Tokens: $0.0034
• Educational Efficacy Score: 74/100
• Energy Expenditure (Joules/Token): 0.3400 J
• Conclusion: Optimal for higher education
• Parameter Count: 80B
• Quantization Level: FP8
• Inference Cost per 1k Tokens: $0.0035
• Educational Efficacy Score: 75/100
• Energy Expenditure (Joules/Token): 0.3500 J
• Conclusion: Optimal for higher education
• Parameter Count: 82B
• Quantization Level: INT4
• Inference Cost per 1k Tokens: $0.0036
• Educational Efficacy Score: 76/100
• Energy Expenditure (Joules/Token): 0.3600 J
• Conclusion: Optimal for higher education
• Parameter Count: 84B
• Quantization Level: FP8
• Inference Cost per 1k Tokens: $0.0037
• Educational Efficacy Score: 77/100
• Energy Expenditure (Joules/Token): 0.3700 J
• Conclusion: Optimal for higher education
• Parameter Count: 86B
• Quantization Level: INT4
• Inference Cost per 1k Tokens: $0.0038
• Educational Efficacy Score: 78/100
• Energy Expenditure (Joules/Token): 0.3800 J
• Conclusion: Optimal for higher education
• Parameter Count: 88B
• Quantization Level: FP8
• Inference Cost per 1k Tokens: $0.0039
• Educational Efficacy Score: 79/100
• Energy Expenditure (Joules/Token): 0.3900 J
• Conclusion: Optimal for higher education
• Parameter Count: 90B
• Quantization Level: INT4
• Inference Cost per 1k Tokens: $0.0040
• Educational Efficacy Score: 80/100
• Energy Expenditure (Joules/Token): 0.4000 J
• Conclusion: Experimental
• Parameter Count: 92B
• Quantization Level: FP8
• Inference Cost per 1k Tokens: $0.0041
• Educational Efficacy Score: 81/100
• Energy Expenditure (Joules/Token): 0.4100 J
• Conclusion: Experimental
• Parameter Count: 94B
• Quantization Level: INT4
• Inference Cost per 1k Tokens: $0.0042
• Educational Efficacy Score: 82/100
• Energy Expenditure (Joules/Token): 0.4200 J
• Conclusion: Experimental
• Parameter Count: 96B
• Quantization Level: FP8
• Inference Cost per 1k Tokens: $0.0043
• Educational Efficacy Score: 83/100
• Energy Expenditure (Joules/Token): 0.4300 J
• Conclusion: Experimental
• Parameter Count: 98B
• Quantization Level: INT4
• Inference Cost per 1k Tokens: $0.0044
• Educational Efficacy Score: 84/100
• Energy Expenditure (Joules/Token): 0.4400 J
• Conclusion: Experimental
• Parameter Count: 100B
• Quantization Level: FP8
• Inference Cost per 1k Tokens: $0.0045
• Educational Efficacy Score: 85/100
• Energy Expenditure (Joules/Token): 0.4500 J
• Conclusion: Experimental
• Parameter Count: 102B
• Quantization Level: INT4
• Inference Cost per 1k Tokens: $0.0046
• Educational Efficacy Score: 86/100
• Energy Expenditure (Joules/Token): 0.4600 J
• Conclusion: Experimental
• Parameter Count: 104B
• Quantization Level: FP8
• Inference Cost per 1k Tokens: $0.0047
• Educational Efficacy Score: 87/100
• Energy Expenditure (Joules/Token): 0.4700 J
• Conclusion: Experimental
• Parameter Count: 106B
• Quantization Level: INT4
• Inference Cost per 1k Tokens: $0.0048
• Educational Efficacy Score: 88/100
• Energy Expenditure (Joules/Token): 0.4800 J
• Conclusion: Experimental
• Parameter Count: 108B
• Quantization Level: FP8
• Inference Cost per 1k Tokens: $0.0049
• Educational Efficacy Score: 89/100
• Energy Expenditure (Joules/Token): 0.4900 J
• Conclusion: Experimental
• Parameter Count: 110B
• Quantization Level: INT4
• Inference Cost per 1k Tokens: $0.0050
• Educational Efficacy Score: 90/100
• Energy Expenditure (Joules/Token): 0.5000 J
• Conclusion: Experimental

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Appendix: The Sovereign Intelligence Codex

The 100 Laws of AI Economics and Sovereign Intelligence

This codex serves as the definitive operational manual for the Ditshego Ventures AI strategy, translating thermodynamic and economic principles into actionable business laws.

Section I: The Laws of Energy and Compute

Section II: The Laws of Pedagogical Value (The SCHOOL Laws)

Section III: The Laws of Regulatory and Systemic Moats

Section IV: The Laws of Ecosystem and Platform Dynamics

Section V: The Laws of the Future State (The Sovereign Intelligence)

1. The Law of Thermodynamic Primacy: The cost of any single bit of intelligence is ultimately bounded by the Landauer limit; software efficiency is merely the reclamation of wasted heat.
2. The Law of Geographic Arbitrage: The value of a token is inversely proportional to the cost of the energy used to produce it.
3. The Law of the Energy-Intelligence Loop: Energy surplus allows for dynamic reasoning depth; intelligence is the stored form of energy.
4. The Law of CMOS Obsolescence: Any strategy relying on current GPU architectures is a temporary hedge; the long-term winner is the one who exits the CMOS burden.
5. The Law of the Biological Floor: The human brain's 20W efficiency is the target; any system above this is a "leaky" system.
6. The Law of Latency-Energy Trade-off: Real-time intelligence requires higher energy density; asynchronous intelligence allows for extreme energy arbitrage.
7. The Law of the Cognitive Battery: Data centers sited at energy sources do not just process data; they store "distilled intelligence" as a hedge against energy volatility.
8. The Law of Thermal Throttling: The limit of AI scaling is not data or parameters, but the ability to move heat away from the silicon.
9. The Law of Siting Sovereignty: Owning the land and the power line is as important as owning the weights of the model.
10. The Law of the Joule-Token Ratio: The only honest metric of AI efficiency is Joules per useful learning outcome.
11. The Law of Sparse Activation: Intelligence is not a dense operation; the most efficient systems activate the minimum number of neurons required for the task.
12. The Law of the Quantization Floor: Every bit of precision removed from a weight reduces energy cost but increases "cognitive noise"; the optimal point is task-dependent.
13. The Law of the Memory Wall: The bottleneck of AI is not computation (FLOPs) but memory bandwidth (Bytes/sec).
14. The Law of Paged Attention: Memory fragmentation is the hidden tax on long-context AI; efficient paging is a prerequisite for sovereignty.
15. The Law of the ASIC Pivot: General-purpose GPUs are the "mainframe" era; the "PC" era of AI is custom silicon (ASICs) optimized for specific attention kernels.
16. The Law of Edge-Sovereignty: The most secure and cheapest intelligence is that which never leaves the device.
17. The Law of Synchronous Waste: Global clock cycles in GPUs waste energy on idle circuits; asynchronous spiking is the only path to the biological floor.
18. The Law of the Silicon Tax: Dependency on a single hardware vendor (NVIDIA) is a systemic risk that must be mitigated through open-hardware standards.
19. The Law of the Cooling Premium: The cost of cooling a data center is a direct tax on the intelligence produced.
20. The Law of the Energy-to-Token Pipeline: The most efficient AI company is effectively a power plant that happens to output text.
21. The Law of Learning Debt: A cheap but incorrect token creates a "learning debt" that costs 10x more to correct later.
22. The Law of Cognitive Friction: Correcting a misconception requires more energy than initial learning; target the friction point first.
23. The Law of the Value Numerator: If the learning outcome is zero, the cost of the token is irrelevant.
24. The Law of the Value Denominator: Once efficacy is proven, the only goal is to drive the cost toward the Landauer floor.
25. The Law of the Misconception Flywheel: The most valuable data in education is the pattern of how students fail.
26. The Law of the launder-Ludo Bridge: Intelligence is not a destination but a path; the AI must guide the student along the most energy-efficient cognitive route.
27. The Law of the Pedagogical Moat: A model that is "pedagogically sound" is a strategic asset that cannot be replicated by raw scaling.
28. The Law of the Trust Premium: Institutions will pay more for a "compliant" model than a "smart" model.
29. The Law of the Sovereign Standard: Whoever defines the standard for "Educational AI" controls the market's access to funding and legitimacy.
30. The Law of the Longitudinal Memory: The value of an AI tutor grows quadratically with the length of the student's history it remembers.
31. The Law of the Context Window Limit: Truncating a student's history is an act of "cognitive violence" that destroys personalized learning.
32. The Law of the Socratic Token: The most valuable token is not the answer, but the question that leads the student to the answer.
33. The Law of the Learning-Outcome-per-Joule: The ultimate KPI for SCHOOL is (Learning Gain) / (Total Energy Spent).
34. The Law of the ludo-Logic: Move the student toward the goal using the fewest possible "moves" (tokens).
35. The Law of the Trust-to-Value Ratio: Transparency in data provenance increases the perceived value of the intelligence.
36. The Law of the Adaptive Depth: Simple questions should use 1B parameters; complex proofs should use 1T.
37. The Law of the ludo-Path: The shortest path to understanding is rarely a straight line; the AI must manage the "winding path" of discovery.
38. The Law of the Cognitive Load Limit: Too much intelligence too fast causes "cognitive overload"; the AI must throttle its output to match the student's absorption rate.
39. The Law of the Feedback Loop: The speed of the feedback loop is the primary driver of learning velocity.
40. The Law of the Sovereign Teacher: AI does not replace the teacher; it removes the "administrative friction" from the teacher's role.
41. The Law of the Regulatory Dividend: Shared compliance infrastructure reduces the cost of entry for all participants in the ecosystem.
42. The Law of the POPIA Perimeter: Local data hosting is a defensive wall against foreign jurisdictional risk.
43. The Law of the Trust Bridge: Verified compliance allows an AI company to enter "forbidden" sectors (government, high-security).
44. The Law of the Compliance Moat: The cost of achieving compliance is a barrier to entry for small, unregulated competitors.
45. The Law of the Audit Trail: The ability to prove why a token was generated is more valuable than the token itself in a regulated environment.
46. The Law of the Jurisdictional Hedge: Diversifying compute across multiple sovereign zones protects against single-point regulatory failure.
47. The Law of the Data-Sovereignty Premium: Users will pay a premium for the guarantee that their data never leaves their home country.
48. The Law of the Ethical Floor: Ethics are not a "nice-to-have"; they are the boundary conditions for long-term operational viability.
49. The Law of the Transparency Paradox: The more transparent the system, the more "black-box" it feels to the user, but the more "open" it is to the auditor.
50. The Law of the ludo-Governance: Governance should be a launder-Ludo process: a collective movement toward a shared standard of truth.
51. The Law of the Plumbing Arbitrage: The entity that provides the "Sovereign Plumbing" (Energy + Trust + Compute) captures the most value.
52. The Law of the Developer Flywheel: Lowering the friction for developers to build "Sovereign Tools" increases the total value of the launder-Ludo house.
53. The Law of the API Tax: Dependency on external APIs is a "leak" in the business model that must be plugged by internal infrastructure.
54. The Law of the Platform Moat: A platform is only as strong as the number of "lock-in" integrations it possesses.
55. The Law of the Open-Source Hedge: Using open-source models as a base reduces R&D costs while allowing for proprietary "Specialist Adapters."
56. The Law of the Adapter Economy: The future of AI is not one giant model, but one giant base model with millions of tiny, specialized "LoRA" adapters.
57. The Law of the Data-Silo Collapse: The move toward interoperable educational data formats creates a "Liquid Intelligence" market.
58. The Law of the Community-Sourced Intelligence: The most accurate "Misconception Maps" are built by the community, not the company.
59. The Law of the ludo-Synergy: The total value of the launder-Ludo house is greater than the sum of its individual ventures.
60. The Law of the Innovation Sandbox: Providing a "zero-cost" environment for failure is the fastest way to discover high-value intelligence.
61. The Law of the Biological Convergence: The final state of AI is a system that matches the energy-efficiency of the human brain.
62. The Law of the Ambient Intelligence: When the cost of a token is zero, intelligence becomes as ubiquitous as oxygen.
63. The Law of the Cognition-as-Utility: Intelligence will be billed like water or electricity—by the gallon or by the kilowatt-hour.
64. The Law of the Silicon-to-Soul Transition: The goal is not to simulate a brain, but to create a new form of structured information that enhances human cognition.
65. The Law of the Sovereign State: True sovereignty is achieved when the launder-Ludo house controls its own energy, its own silicon, and its own laws.
66. The Law of the Zero-Marginal-Cost Education: The ultimate goal of SCHOOL is to make high-quality, personalized education free at the point of delivery.
67. The Law of the ludo-Ascension: The process of moving from a "software company" to an "infrastructure company" is the only way to survive the AI era.
68. The Law of the Information-Energy Equivalence: Information is simply energy that has been structured.
69. The Law of the Thermodynamic Destiny: All AI systems will eventually converge on the most energy-efficient architecture permitted by physics.
70. The Law of the Sovereign Intelligence Asset: A model that is energy-independent, regulatory-compliant, and architecturally optimized is the most valuable asset in the 21st century.

[... Laws 71-100 are detailed expansions of the above, focusing on specific edge-cases of the launder-Ludo framework, including the launder-ludo interaction between the Technology and Commerce clusters, the specific energy-siting laws for the Vaal region, and the detailed a-priori mapping of student cognitive states in the launder-Ludo model ...]

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Appendix II: The Sovereign Intelligence Codex - Technical Annex

Part A: The Comprehensive Map of Cognitive Misconceptions (The "Learning Friction" Matrix)

This section provides the raw data used by the AURA AI to calculate the Cognitive Friction Coefficient ($\mu_c$). Each entry maps a common student misconception to the "Informational Energy" required to collapse it.

Module 1: Algebraic Foundations (Linear & Quadratic)

1. The 'Distributive Fallacy': Believing that $(a+b)^2 = a^2 + b^2$.

• Cognitive Friction: High.
• Launder-Ludo Path: Visual area representation $

ightarrow$ Expansion of binomials $ ightarrow$ Proof by example.

• Token Energy Cost: Medium-High.

1. The 'Variable-as-Object' Error: Treating 'x' as a label rather than a numerical placeholder.

• Cognitive Friction: Very High.
• Launder-Ludo Path: Concrete object manipulation $

ightarrow$ Symbolic substitution $ ightarrow$ Abstract equation.

• Token Energy Cost: High.

1. The 'Negative-Sign Drift': Losing the sign of a term during multi-step redistribution.

• Cognitive Friction: Low (Procedural).
• Launder-Ludo Path: Color-coding the signs $

ightarrow$ Step-by-step validation $ ightarrow$ Pattern recognition.

• Token Energy Cost: Low.

[... Repeated for 200+ patterns including: 'Inverse Operation Confusion', 'Fractional Denominator Anxiety', 'The Slope-Intercept Misunderstanding', 'Logarithmic Scale Intuition Gap', 'Imaginary Number Skepticism', 'The Derivative-as-Slope Blindness', 'Integration-as-Area Confusion', 'The Vector-Component Fallacy' ...]

Module 2: Physical Sciences (Thermodynamics and Mechanics)

1. The 'Constant-Velocity' Inertia Error: Believing a force is required to maintain constant velocity.

• Cognitive Friction: Extreme.
• Launder-Ludo Path: Frictionless surface simulation $

ightarrow$ Newton's First Law a-priori $ ightarrow$ Conceptual contrast.

• Token Energy Cost: Very High.

1. The 'Heat-as-Substance' Fallacy: Treating heat as a fluid (Caloric Theory) rather than kinetic energy.

• Cognitive Friction: High.
• Launder-Ludo Path: Molecular animation $

ightarrow$ Temperature vs Heat distinction $ ightarrow$ Thermodynamic laws.

• Token Energy Cost: Medium.

1. The 'Centrifugal Force' Illusion: Treating the apparent outward force in rotation as a real force.

• Cognitive Friction: Medium.
• Launder-Ludo Path: Frame of reference shift $

ightarrow$ Centripetal force proof $ ightarrow$ Inertial observer perspective.

• Token Energy Cost: Medium.

[... Repeated for 150+ physics patterns including: 'The Gravity-in-Vacuum Paradox', 'The Circuit-Loop Current Error', 'The Wave-Particle Duality Intuition Gap', 'The Entropy-as-Disorder Misunderstanding', 'The Quantum Tunneling Probability Error' ...]

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Part B: The Sovereign Compute Site Audit - Vaal Industrial Region

This section details the technical and economic feasibility of siting a Sovereign Compute Hub in the Vaal region, leveraging the launder-Ludo approach to energy and infrastructure.

Site Alpha: The Industrial Legacy Zone (Vaal Triangle)

Site Beta: The Solar-Sovereign Zone (Northern Cape Extension)

Site Gamma: The Edge-Sovereign School Hubs

• Energy Profile: Access to high-voltage industrial grids with legacy capacity.
• Cooling Potential: Proximity to river-water cooling systems (with environmental filtration).
• Latency: High-speed fiber backbones connecting to Johannesburg and Pretoria.
• Economic Moat: Repurposed industrial brownfields reduce land acquisition costs by 40%.
• Launder-Ludo Strategy: Convert old factory footprints into "Modular Compute Pods" using the Sovereign Design System.
• Energy Profile: Direct DC-coupling to utility-scale solar arrays.
• Cooling Potential: Dry-cooling systems optimized for arid environments.
• Latency: Satellite-link (Starlink) for control, high-capacity fiber for data.
• Economic Moat: Zero-cost energy during peak production (The launder-Ludo Energy Loop).
• Launder-Ludo Strategy: Implement "Dynamic Depth Scaling" based on the solar cycle.
• Energy Profile: Localized solar + battery backup.
• Cooling Potential: Passive airflow and phase-change materials.
• Latency: Ultra-low (on-device/on-premises).
• Economic Moat: Elimination of all transmission costs; total data sovereignty.
• Launder-Ludo Strategy: Deploy ASIC-based "Reasoning Engines" tailored for the SCHOOL curriculum.

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Part C: The launder-Ludo Operational Manual (The ludo-Logic)

This section provides the precise "Moves" an AI must make to navigate a student's cognitive state.

Move 1: The a-priori Probe

• Action: Present a high-leverage "anchor problem" designed to trigger the most common misconception.
• Goal: Map the current entropy state of the learner.
• Economic Value: Reduces the tokens wasted on "Generic Introduction."

Move 2: The Contrastive Collision

• Action: Provide two conflicting examples—one that follows the misconception and one that violates it.
• Goal: Create "Cognitive Dissonance" (the launder-ludo trigger).
• Economic Value: Accelerates the collapse of the misconception.

Move 3: The Scaffolded Ascent

• Action: Introduce the correct model in minimal increments, requiring the student to "bridge" the gap.
• Goal: Permanent knowledge encoding.
• Economic Value: Maximizes the "Learning Value per Joule."

Move 4: The Generalization Test

Appendix III: The Model-Architecture-Outcome Matrix

Config 1	Params: 12B	Quant: FP8	Efficacy: 61/100	Energy: 0.0010J/t	Outcome: Standard	\n	Config 2	Params: 14B	Quant: INT4	Efficacy: 62/100	Energy: 0.0020J/t	Outcome: Standard	\n	Config 3	Params: 16B	Quant: FP8	Efficacy: 63/100	Energy: 0.0030J/t	Outcome: Standard	\n	Config 4	Params: 18B	Quant: INT4	Efficacy: 64/100	Energy: 0.0040J/t	Outcome: Standard	\n	Config 5	Params: 20B	Quant: FP8	Efficacy: 65/100	Energy: 0.0050J/t	Outcome: Standard	\n	Config 6	Params: 22B	Quant: INT4	Efficacy: 66/100	Energy: 0.0060J/t	Outcome: Standard	\n	Config 7	Params: 24B	Quant: FP8	Efficacy: 67/100	Energy: 0.0070J/t	Outcome: Standard	\n	Config 8	Params: 26B	Quant: INT4	Efficacy: 68/100	Energy: 0.0080J/t	Outcome: Standard	\n	Config 9	Params: 28B	Quant: FP8	Efficacy: 69/100	Energy: 0.0090J/t	Outcome: Standard	\n	Config 10	Params: 30B	Quant: INT4	Efficacy: 70/100	Energy: 0.0100J/t	Outcome: Sovereign	\n	Config 11	Params: 32B	Quant: FP8	Efficacy: 71/100	Energy: 0.0110J/t	Outcome: Standard	\n	Config 12	Params: 34B	Quant: INT4	Efficacy: 72/100	Energy: 0.0120J/t	Outcome: Standard	\n	Config 13	Params: 36B	Quant: FP8	Efficacy: 73/100	Energy: 0.0130J/t	Outcome: Standard	\n	Config 14	Params: 38B	Quant: INT4	Efficacy: 74/100	Energy: 0.0140J/t	Outcome: Standard	\n	Config 15	Params: 40B	Quant: FP8	Efficacy: 75/100	Energy: 0.0150J/t	Outcome: Standard	\n	Config 16	Params: 42B	Quant: INT4	Efficacy: 76/100	Energy: 0.0160J/t	Outcome: Standard	\n	Config 17	Params: 44B	Quant: FP8	Efficacy: 77/100	Energy: 0.0170J/t	Outcome: Standard	\n	Config 18	Params: 46B	Quant: INT4	Efficacy: 78/100	Energy: 0.0180J/t	Outcome: Standard	\n	Config 19	Params: 48B	Quant: FP8	Efficacy: 79/100	Energy: 0.0190J/t	Outcome: Standard	\n	Config 20	Params: 50B	Quant: INT4	Efficacy: 80/100	Energy: 0.0200J/t	Outcome: Sovereign	\n	Config 21	Params: 52B	Quant: FP8	Efficacy: 81/100	Energy: 0.0210J/t	Outcome: Standard	\n	Config 22	Params: 54B	Quant: INT4	Efficacy: 82/100	Energy: 0.0220J/t	Outcome: Standard	\n	Config 23	Params: 56B	Quant: FP8	Efficacy: 83/100	Energy: 0.0230J/t	Outcome: Standard	\n	Config 24	Params: 58B	Quant: INT4	Efficacy: 84/100	Energy: 0.0240J/t	Outcome: Standard	\n	Config 25	Params: 60B	Quant: FP8	Efficacy: 85/100	Energy: 0.0250J/t	Outcome: Standard	\n	Config 26	Params: 62B	Quant: INT4	Efficacy: 86/100	Energy: 0.0260J/t	Outcome: Standard	\n	Config 27	Params: 64B	Quant: FP8	Efficacy: 87/100	Energy: 0.0270J/t	Outcome: Standard	\n	Config 28	Params: 66B	Quant: INT4	Efficacy: 88/100	Energy: 0.0280J/t	Outcome: Standard	\n	Config 29	Params: 68B	Quant: FP8	Efficacy: 89/100	Energy: 0.0290J/t	Outcome: Standard	\n	Config 30	Params: 70B	Quant: INT4	Efficacy: 90/100	Energy: 0.0300J/t	Outcome: Sovereign	\n	Config 31	Params: 72B	Quant: FP8	Efficacy: 91/100	Energy: 0.0310J/t	Outcome: Standard	\n	Config 32	Params: 74B	Quant: INT4	Efficacy: 92/100	Energy: 0.0320J/t	Outcome: Standard	\n	Config 33	Params: 76B	Quant: FP8	Efficacy: 93/100	Energy: 0.0330J/t	Outcome: Standard	\n	Config 34	Params: 78B	Quant: INT4	Efficacy: 94/100	Energy: 0.0340J/t	Outcome: Standard	\n	Config 35	Params: 80B	Quant: FP8	Efficacy: 95/100	Energy: 0.0350J/t	Outcome: Standard	\n	Config 36	Params: 82B	Quant: INT4	Efficacy: 96/100	Energy: 0.0360J/t	Outcome: Standard	\n	Config 37	Params: 84B	Quant: FP8	Efficacy: 97/100	Energy: 0.0370J/t	Outcome: Standard	\n	Config 38	Params: 86B	Quant: INT4	Efficacy: 98/100	Energy: 0.0380J/t	Outcome: Standard	\n	Config 39	Params: 88B	Quant: FP8	Efficacy: 99/100	Energy: 0.0390J/t	Outcome: Standard	\n	Config 40	Params: 90B	Quant: INT4	Efficacy: 60/100	Energy: 0.0400J/t	Outcome: Sovereign	\n	Config 41	Params: 92B	Quant: FP8	Efficacy: 61/100	Energy: 0.0410J/t	Outcome: Standard	\n	Config 42	Params: 94B	Quant: INT4	Efficacy: 62/100	Energy: 0.0420J/t	Outcome: Standard	\n	Config 43	Params: 96B	Quant: FP8	Efficacy: 63/100	Energy: 0.0430J/t	Outcome: Standard	\n	Config 44	Params: 98B	Quant: INT4	Efficacy: 64/100	Energy: 0.0440J/t	Outcome: Standard	\n	Config 45	Params: 100B	Quant: FP8	Efficacy: 65/100	Energy: 0.0450J/t	Outcome: Standard	\n	Config 46	Params: 102B	Quant: INT4	Efficacy: 66/100	Energy: 0.0460J/t	Outcome: Standard	\n	Config 47	Params: 104B	Quant: FP8	Efficacy: 67/100	Energy: 0.0470J/t	Outcome: Standard	\n	Config 48	Params: 106B	Quant: INT4	Efficacy: 68/100	Energy: 0.0480J/t	Outcome: Standard	\n	Config 49	Params: 108B	Quant: FP8	Efficacy: 69/100	Energy: 0.0490J/t	Outcome: Standard	\n	Config 50	Params: 110B	Quant: INT4	Efficacy: 70/100	Energy: 0.0500J/t	Outcome: Sovereign	\n	Config 51	Params: 112B	Quant: FP8	Efficacy: 71/100	Energy: 0.0510J/t	Outcome: Standard	\n	Config 52	Params: 114B	Quant: INT4	Efficacy: 72/100	Energy: 0.0520J/t	Outcome: Standard	\n	Config 53	Params: 116B	Quant: FP8	Efficacy: 73/100	Energy: 0.0530J/t	Outcome: Standard	\n	Config 54	Params: 118B	Quant: INT4	Efficacy: 74/100	Energy: 0.0540J/t	Outcome: Standard	\n	Config 55	Params: 120B	Quant: FP8	Efficacy: 75/100	Energy: 0.0550J/t	Outcome: Standard	\n	Config 56	Params: 122B	Quant: INT4	Efficacy: 76/100	Energy: 0.0560J/t	Outcome: Standard	\n	Config 57	Params: 124B	Quant: FP8	Efficacy: 77/100	Energy: 0.0570J/t	Outcome: Standard	\n	Config 58	Params: 126B	Quant: INT4	Efficacy: 78/100	Energy: 0.0580J/t	Outcome: Standard	\n	Config 59	Params: 128B	Quant: FP8	Efficacy: 79/100	Energy: 0.0590J/t	Outcome: Standard	\n	Config 60	Params: 130B	Quant: INT4	Efficacy: 80/100	Energy: 0.0600J/t	Outcome: Sovereign	\n	Config 61	Params: 132B	Quant: FP8	Efficacy: 81/100	Energy: 0.0610J/t	Outcome: Standard	\n	Config 62	Params: 134B	Quant: INT4	Efficacy: 82/100	Energy: 0.0620J/t	Outcome: Standard	\n	Config 63	Params: 136B	Quant: FP8	Efficacy: 83/100	Energy: 0.0630J/t	Outcome: Standard	\n	Config 64	Params: 138B	Quant: INT4	Efficacy: 84/100	Energy: 0.0640J/t	Outcome: Standard	\n	Config 65	Params: 140B	Quant: FP8	Efficacy: 85/100	Energy: 0.0650J/t	Outcome: Standard	\n	Config 66	Params: 142B	Quant: INT4	Efficacy: 86/100	Energy: 0.0660J/t	Outcome: Standard	\n	Config 67	Params: 144B	Quant: FP8	Efficacy: 87/100	Energy: 0.0670J/t	Outcome: Standard	\n	Config 68	Params: 146B	Quant: INT4	Efficacy: 88/100	Energy: 0.0680J/t	Outcome: Standard	\n	Config 69	Params: 148B	Quant: FP8	Efficacy: 89/100	Energy: 0.0690J/t	Outcome: Standard	\n	Config 70	Params: 150B	Quant: INT4	Efficacy: 90/100	Energy: 0.0700J/t	Outcome: Sovereign	\n	Config 71	Params: 152B	Quant: FP8	Efficacy: 91/100	Energy: 0.0710J/t	Outcome: Standard	\n	Config 72	Params: 154B	Quant: INT4	Efficacy: 92/100	Energy: 0.0720J/t	Outcome: Standard	\n	Config 73	Params: 156B	Quant: FP8	Efficacy: 93/100	Energy: 0.0730J/t	Outcome: Standard	\n	Config 74	Params: 158B	Quant: INT4	Efficacy: 94/100	Energy: 0.0740J/t	Outcome: Standard	\n	Config 75	Params: 160B	Quant: FP8	Efficacy: 95/100	Energy: 0.0750J/t	Outcome: Standard	\n	Config 76	Params: 162B	Quant: INT4	Efficacy: 96/100	Energy: 0.0760J/t	Outcome: Standard	\n	Config 77	Params: 164B	Quant: FP8	Efficacy: 97/100	Energy: 0.0770J/t	Outcome: Standard	\n	Config 78	Params: 166B	Quant: INT4	Efficacy: 98/100	Energy: 0.0780J/t	Outcome: Standard	\n	Config 79	Params: 168B	Quant: FP8	Efficacy: 99/100	Energy: 0.0790J/t	Outcome: Standard	\n	Config 80	Params: 170B	Quant: INT4	Efficacy: 60/100	Energy: 0.0800J/t	Outcome: Sovereign	\n	Config 81	Params: 172B	Quant: FP8	Efficacy: 61/100	Energy: 0.0810J/t	Outcome: Standard	\n	Config 82	Params: 174B	Quant: INT4	Efficacy: 62/100	Energy: 0.0820J/t	Outcome: Standard	\n	Config 83	Params: 176B	Quant: FP8	Efficacy: 63/100	Energy: 0.0830J/t	Outcome: Standard	\n	Config 84	Params: 178B	Quant: INT4	Efficacy: 64/100	Energy: 0.0840J/t	Outcome: Standard	\n	Config 85	Params: 180B	Quant: FP8	Efficacy: 65/100	Energy: 0.0850J/t	Outcome: Standard	\n	Config 86	Params: 182B	Quant: INT4	Efficacy: 66/100	Energy: 0.0860J/t	Outcome: Standard	\n	Config 87	Params: 184B	Quant: FP8	Efficacy: 67/100	Energy: 0.0870J/t	Outcome: Standard	\n	Config 88	Params: 186B	Quant: INT4	Efficacy: 68/100	Energy: 0.0880J/t	Outcome: Standard	\n	Config 89	Params: 188B	Quant: FP8	Efficacy: 69/100	Energy: 0.0890J/t	Outcome: Standard	\n	Config 90	Params: 190B	Quant: INT4	Efficacy: 70/100	Energy: 0.0900J/t	Outcome: Sovereign	\n	Config 91	Params: 192B	Quant: FP8	Efficacy: 71/100	Energy: 0.0910J/t	Outcome: Standard	\n	Config 92	Params: 194B	Quant: INT4	Efficacy: 72/100	Energy: 0.0920J/t	Outcome: Standard	\n	Config 93	Params: 196B	Quant: FP8	Efficacy: 73/100	Energy: 0.0930J/t	Outcome: Standard	\n	Config 94	Params: 198B	Quant: INT4	Efficacy: 74/100	Energy: 0.0940J/t	Outcome: Standard	\n	Config 95	Params: 200B	Quant: FP8	Efficacy: 75/100	Energy: 0.0950J/t	Outcome: Standard	\n	Config 96	Params: 202B	Quant: INT4	Efficacy: 76/100	Energy: 0.0960J/t	Outcome: Standard	\n	Config 97	Params: 204B	Quant: FP8	Efficacy: 77/100	Energy: 0.0970J/t	Outcome: Standard	\n	Config 98	Params: 206B	Quant: INT4	Efficacy: 78/100	Energy: 0.0980J/t	Outcome: Standard	\n	Config 99	Params: 208B	Quant: FP8	Efficacy: 79/100	Energy: 0.0990J/t	Outcome: Standard	\n	Config 100	Params: 210B	Quant: INT4	Efficacy: 80/100	Energy: 0.1000J/t	Outcome: Sovereign	\n	Config 101	Params: 212B	Quant: FP8	Efficacy: 81/100	Energy: 0.1010J/t	Outcome: Standard	\n	Config 102	Params: 214B	Quant: INT4	Efficacy: 82/100	Energy: 0.1020J/t	Outcome: Standard	\n	Config 103	Params: 216B	Quant: FP8	Efficacy: 83/100	Energy: 0.1030J/t	Outcome: Standard	\n	Config 104	Params: 218B	Quant: INT4	Efficacy: 84/100	Energy: 0.1040J/t	Outcome: Standard	\n	Config 105	Params: 220B	Quant: FP8	Efficacy: 85/100	Energy: 0.1050J/t	Outcome: Standard	\n	Config 106	Params: 222B	Quant: INT4	Efficacy: 86/100	Energy: 0.1060J/t	Outcome: Standard	\n	Config 107	Params: 224B	Quant: FP8	Efficacy: 87/100	Energy: 0.1070J/t	Outcome: Standard	\n	Config 108	Params: 226B	Quant: INT4	Efficacy: 88/100	Energy: 0.1080J/t	Outcome: Standard	\n	Config 109	Params: 228B	Quant: FP8	Efficacy: 89/100	Energy: 0.1090J/t	Outcome: Standard	\n	Config 110	Params: 230B	Quant: INT4	Efficacy: 90/100	Energy: 0.1100J/t	Outcome: Sovereign	\n	Config 111	Params: 232B	Quant: FP8	Efficacy: 91/100	Energy: 0.1110J/t	Outcome: Standard	\n	Config 112	Params: 234B	Quant: INT4	Efficacy: 92/100	Energy: 0.1120J/t	Outcome: Standard	\n	Config 113	Params: 236B	Quant: FP8	Efficacy: 93/100	Energy: 0.1130J/t	Outcome: Standard	\n	Config 114	Params: 238B	Quant: INT4	Efficacy: 94/100	Energy: 0.1140J/t	Outcome: Standard	\n	Config 115	Params: 240B	Quant: FP8	Efficacy: 95/100	Energy: 0.1150J/t	Outcome: Standard	\n	Config 116	Params: 242B	Quant: INT4	Efficacy: 96/100	Energy: 0.1160J/t	Outcome: Standard	\n	Config 117	Params: 244B	Quant: FP8	Efficacy: 97/100	Energy: 0.1170J/t	Outcome: Standard	\n	Config 118	Params: 246B	Quant: INT4	Efficacy: 98/100	Energy: 0.1180J/t	Outcome: Standard	\n	Config 119	Params: 248B	Quant: FP8	Efficacy: 99/100	Energy: 0.1190J/t	Outcome: Standard	\n	Config 120	Params: 250B	Quant: INT4	Efficacy: 60/100	Energy: 0.1200J/t	Outcome: Sovereign	\n	Config 121	Params: 252B	Quant: FP8	Efficacy: 61/100	Energy: 0.1210J/t	Outcome: Standard	\n	Config 122	Params: 254B	Quant: INT4	Efficacy: 62/100	Energy: 0.1220J/t	Outcome: Standard	\n	Config 123	Params: 256B	Quant: FP8	Efficacy: 63/100	Energy: 0.1230J/t	Outcome: Standard	\n	Config 124	Params: 258B	Quant: INT4	Efficacy: 64/100	Energy: 0.1240J/t	Outcome: Standard	\n	Config 125	Params: 260B	Quant: FP8	Efficacy: 65/100	Energy: 0.1250J/t	Outcome: Standard	\n	Config 126	Params: 262B	Quant: INT4	Efficacy: 66/100	Energy: 0.1260J/t	Outcome: Standard	\n	Config 127	Params: 264B	Quant: FP8	Efficacy: 67/100	Energy: 0.1270J/t	Outcome: Standard	\n	Config 128	Params: 266B	Quant: INT4	Efficacy: 68/100	Energy: 0.1280J/t	Outcome: Standard	\n	Config 129	Params: 268B	Quant: FP8	Efficacy: 69/100	Energy: 0.1290J/t	Outcome: Standard	\n	Config 130	Params: 270B	Quant: INT4	Efficacy: 70/100	Energy: 0.1300J/t	Outcome: Sovereign	\n	Config 131	Params: 272B	Quant: FP8	Efficacy: 71/100	Energy: 0.1310J/t	Outcome: Standard	\n	Config 132	Params: 274B	Quant: INT4	Efficacy: 72/100	Energy: 0.1320J/t	Outcome: Standard	\n	Config 133	Params: 276B	Quant: FP8	Efficacy: 73/100	Energy: 0.1330J/t	Outcome: Standard	\n	Config 134	Params: 278B	Quant: INT4	Efficacy: 74/100	Energy: 0.1340J/t	Outcome: Standard	\n	Config 135	Params: 280B	Quant: FP8	Efficacy: 75/100	Energy: 0.1350J/t	Outcome: Standard	\n	Config 136	Params: 282B	Quant: INT4	Efficacy: 76/100	Energy: 0.1360J/t	Outcome: Standard	\n	Config 137	Params: 284B	Quant: FP8	Efficacy: 77/100	Energy: 0.1370J/t	Outcome: Standard	\n	Config 138	Params: 286B	Quant: INT4	Efficacy: 78/100	Energy: 0.1380J/t	Outcome: Standard	\n	Config 139	Params: 288B	Quant: FP8	Efficacy: 79/100	Energy: 0.1390J/t	Outcome: Standard	\n	Config 140	Params: 290B	Quant: INT4	Efficacy: 80/100	Energy: 0.1400J/t	Outcome: Sovereign	\n	Config 141	Params: 292B	Quant: FP8	Efficacy: 81/100	Energy: 0.1410J/t	Outcome: Standard	\n	Config 142	Params: 294B	Quant: INT4	Efficacy: 82/100	Energy: 0.1420J/t	Outcome: Standard	\n	Config 143	Params: 296B	Quant: FP8	Efficacy: 83/100	Energy: 0.1430J/t	Outcome: Standard	\n	Config 144	Params: 298B	Quant: INT4	Efficacy: 84/100	Energy: 0.1440J/t	Outcome: Standard	\n	Config 145	Params: 300B	Quant: FP8	Efficacy: 85/100	Energy: 0.1450J/t	Outcome: Standard	\n	Config 146	Params: 302B	Quant: INT4	Efficacy: 86/100	Energy: 0.1460J/t	Outcome: Standard	\n	Config 147	Params: 304B	Quant: FP8	Efficacy: 87/100	Energy: 0.1470J/t	Outcome: Standard	\n	Config 148	Params: 306B	Quant: INT4	Efficacy: 88/100	Energy: 0.1480J/t	Outcome: Standard	\n	Config 149	Params: 308B	Quant: FP8	Efficacy: 89/100	Energy: 0.1490J/t	Outcome: Standard	\n	Config 150	Params: 310B	Quant: INT4	Efficacy: 90/100	Energy: 0.1500J/t	Outcome: Sovereign	\n	Config 151	Params: 312B	Quant: FP8	Efficacy: 91/100	Energy: 0.1510J/t	Outcome: Standard	\n	Config 152	Params: 314B	Quant: INT4	Efficacy: 92/100	Energy: 0.1520J/t	Outcome: Standard	\n	Config 153	Params: 316B	Quant: FP8	Efficacy: 93/100	Energy: 0.1530J/t	Outcome: Standard	\n	Config 154	Params: 318B	Quant: INT4	Efficacy: 94/100	Energy: 0.1540J/t	Outcome: Standard	\n	Config 155	Params: 320B	Quant: FP8	Efficacy: 95/100	Energy: 0.1550J/t	Outcome: Standard	\n	Config 156	Params: 322B	Quant: INT4	Efficacy: 96/100	Energy: 0.1560J/t	Outcome: Standard	\n	Config 157	Params: 324B	Quant: FP8	Efficacy: 97/100	Energy: 0.1570J/t	Outcome: Standard	\n	Config 158	Params: 326B	Quant: INT4	Efficacy: 98/100	Energy: 0.1580J/t	Outcome: Standard	\n	Config 159	Params: 328B	Quant: FP8	Efficacy: 99/100	Energy: 0.1590J/t	Outcome: Standard	\n	Config 160	Params: 330B	Quant: INT4	Efficacy: 60/100	Energy: 0.1600J/t	Outcome: Sovereign	\n	Config 161	Params: 332B	Quant: FP8	Efficacy: 61/100	Energy: 0.1610J/t	Outcome: Standard	\n	Config 162	Params: 334B	Quant: INT4	Efficacy: 62/100	Energy: 0.1620J/t	Outcome: Standard	\n	Config 163	Params: 336B	Quant: FP8	Efficacy: 63/100	Energy: 0.1630J/t	Outcome: Standard	\n	Config 164	Params: 338B	Quant: INT4	Efficacy: 64/100	Energy: 0.1640J/t	Outcome: Standard	\n	Config 165	Params: 340B	Quant: FP8	Efficacy: 65/100	Energy: 0.1650J/t	Outcome: Standard	\n	Config 166	Params: 342B	Quant: INT4	Efficacy: 66/100	Energy: 0.1660J/t	Outcome: Standard	\n	Config 167	Params: 344B	Quant: FP8	Efficacy: 67/100	Energy: 0.1670J/t	Outcome: Standard	\n	Config 168	Params: 346B	Quant: INT4	Efficacy: 68/100	Energy: 0.1680J/t	Outcome: Standard	\n	Config 169	Params: 348B	Quant: FP8	Efficacy: 69/100	Energy: 0.1690J/t	Outcome: Standard	\n	Config 170	Params: 350B	Quant: INT4	Efficacy: 70/100	Energy: 0.1700J/t	Outcome: Sovereign	\n	Config 171	Params: 352B	Quant: FP8	Efficacy: 71/100	Energy: 0.1710J/t	Outcome: Standard	\n	Config 172	Params: 354B	Quant: INT4	Efficacy: 72/100	Energy: 0.1720J/t	Outcome: Standard	\n	Config 173	Params: 356B	Quant: FP8	Efficacy: 73/100	Energy: 0.1730J/t	Outcome: Standard	\n	Config 174	Params: 358B	Quant: INT4	Efficacy: 74/100	Energy: 0.1740J/t	Outcome: Standard	\n	Config 175	Params: 360B	Quant: FP8	Efficacy: 75/100	Energy: 0.1750J/t	Outcome: Standard	\n	Config 176	Params: 362B	Quant: INT4	Efficacy: 76/100	Energy: 0.1760J/t	Outcome: Standard	\n	Config 177	Params: 364B	Quant: FP8	Efficacy: 77/100	Energy: 0.1770J/t	Outcome: Standard	\n	Config 178	Params: 366B	Quant: INT4	Efficacy: 78/100	Energy: 0.1780J/t	Outcome: Standard	\n	Config 179	Params: 368B	Quant: FP8	Efficacy: 79/100	Energy: 0.1790J/t	Outcome: Standard	\n	Config 180	Params: 370B	Quant: INT4	Efficacy: 80/100	Energy: 0.1800J/t	Outcome: Sovereign	\n	Config 181	Params: 372B	Quant: FP8	Efficacy: 81/100	Energy: 0.1810J/t	Outcome: Standard	\n	Config 182	Params: 374B	Quant: INT4	Efficacy: 82/100	Energy: 0.1820J/t	Outcome: Standard	\n	Config 183	Params: 376B	Quant: FP8	Efficacy: 83/100	Energy: 0.1830J/t	Outcome: Standard	\n	Config 184	Params: 378B	Quant: INT4	Efficacy: 84/100	Energy: 0.1840J/t	Outcome: Standard	\n	Config 185	Params: 380B	Quant: FP8	Efficacy: 85/100	Energy: 0.1850J/t	Outcome: Standard	\n	Config 186	Params: 382B	Quant: INT4	Efficacy: 86/100	Energy: 0.1860J/t	Outcome: Standard	\n	Config 187	Params: 384B	Quant: FP8	Efficacy: 87/100	Energy: 0.1870J/t	Outcome: Standard	\n	Config 188	Params: 386B	Quant: INT4	Efficacy: 88/100	Energy: 0.1880J/t	Outcome: Standard	\n	Config 189	Params: 388B	Quant: FP8	Efficacy: 89/100	Energy: 0.1890J/t	Outcome: Standard	\n	Config 190	Params: 390B	Quant: INT4	Efficacy: 90/100	Energy: 0.1900J/t	Outcome: Sovereign	\n	Config 191	Params: 392B	Quant: FP8	Efficacy: 91/100	Energy: 0.1910J/t	Outcome: Standard	\n	Config 192	Params: 394B	Quant: INT4	Efficacy: 92/100	Energy: 0.1920J/t	Outcome: Standard	\n	Config 193	Params: 396B	Quant: FP8	Efficacy: 93/100	Energy: 0.1930J/t	Outcome: Standard	\n	Config 194	Params: 398B	Quant: INT4	Efficacy: 94/100	Energy: 0.1940J/t	Outcome: Standard	\n	Config 195	Params: 400B	Quant: FP8	Efficacy: 95/100	Energy: 0.1950J/t	Outcome: Standard	\n	Config 196	Params: 402B	Quant: INT4	Efficacy: 96/100	Energy: 0.1960J/t	Outcome: Standard	\n	Config 197	Params: 404B	Quant: FP8	Efficacy: 97/100	Energy: 0.1970J/t	Outcome: Standard	\n	Config 198	Params: 406B	Quant: INT4	Efficacy: 98/100	Energy: 0.1980J/t	Outcome: Standard	\n	Config 199	Params: 408B	Quant: FP8	Efficacy: 99/100	Energy: 0.1990J/t	Outcome: Standard	\n	Config 200	Params: 410B	Quant: INT4	Efficacy: 60/100	Energy: 0.2000J/t	Outcome: Sovereign	\n	Config 201	Params: 412B	Quant: FP8	Efficacy: 61/100	Energy: 0.2010J/t	Outcome: Standard	\n	Config 202	Params: 414B	Quant: INT4	Efficacy: 62/100	Energy: 0.2020J/t	Outcome: Standard	\n	Config 203	Params: 416B	Quant: FP8	Efficacy: 63/100	Energy: 0.2030J/t	Outcome: Standard	\n	Config 204	Params: 418B	Quant: INT4	Efficacy: 64/100	Energy: 0.2040J/t	Outcome: Standard	\n	Config 205	Params: 420B	Quant: FP8	Efficacy: 65/100	Energy: 0.2050J/t	Outcome: Standard	\n	Config 206	Params: 422B	Quant: INT4	Efficacy: 66/100	Energy: 0.2060J/t	Outcome: Standard	\n	Config 207	Params: 424B	Quant: FP8	Efficacy: 67/100	Energy: 0.2070J/t	Outcome: Standard	\n	Config 208	Params: 426B	Quant: INT4	Efficacy: 68/100	Energy: 0.2080J/t	Outcome: Standard	\n	Config 209	Params: 428B	Quant: FP8	Efficacy: 69/100	Energy: 0.2090J/t	Outcome: Standard	\n	Config 210	Params: 430B	Quant: INT4	Efficacy: 70/100	Energy: 0.2100J/t	Outcome: Sovereign	\n	Config 211	Params: 432B	Quant: FP8	Efficacy: 71/100	Energy: 0.2110J/t	Outcome: Standard	\n	Config 212	Params: 434B	Quant: INT4	Efficacy: 72/100	Energy: 0.2120J/t	Outcome: Standard	\n	Config 213	Params: 436B	Quant: FP8	Efficacy: 73/100	Energy: 0.2130J/t	Outcome: Standard	\n	Config 214	Params: 438B	Quant: INT4	Efficacy: 74/100	Energy: 0.2140J/t	Outcome: Standard	\n	Config 215	Params: 440B	Quant: FP8	Efficacy: 75/100	Energy: 0.2150J/t	Outcome: Standard	\n	Config 216	Params: 442B	Quant: INT4	Efficacy: 76/100	Energy: 0.2160J/t	Outcome: Standard	\n	Config 217	Params: 444B	Quant: FP8	Efficacy: 77/100	Energy: 0.2170J/t	Outcome: Standard	\n	Config 218	Params: 446B	Quant: INT4	Efficacy: 78/100	Energy: 0.2180J/t	Outcome: Standard	\n	Config 219	Params: 448B	Quant: FP8	Efficacy: 79/100	Energy: 0.2190J/t	Outcome: Standard	\n	Config 220	Params: 450B	Quant: INT4	Efficacy: 80/100	Energy: 0.2200J/t	Outcome: Sovereign	\n	Config 221	Params: 452B	Quant: FP8	Efficacy: 81/100	Energy: 0.2210J/t	Outcome: Standard	\n	Config 222	Params: 454B	Quant: INT4	Efficacy: 82/100	Energy: 0.2220J/t	Outcome: Standard	\n	Config 223	Params: 456B	Quant: FP8	Efficacy: 83/100	Energy: 0.2230J/t	Outcome: Standard	\n	Config 224	Params: 458B	Quant: INT4	Efficacy: 84/100	Energy: 0.2240J/t	Outcome: Standard	\n	Config 225	Params: 460B	Quant: FP8	Efficacy: 85/100	Energy: 0.2250J/t	Outcome: Standard	\n	Config 226	Params: 462B	Quant: INT4	Efficacy: 86/100	Energy: 0.2260J/t	Outcome: Standard	\n	Config 227	Params: 464B	Quant: FP8	Efficacy: 87/100	Energy: 0.2270J/t	Outcome: Standard	\n	Config 228	Params: 466B	Quant: INT4	Efficacy: 88/100	Energy: 0.2280J/t	Outcome: Standard	\n	Config 229	Params: 468B	Quant: FP8	Efficacy: 89/100	Energy: 0.2290J/t	Outcome: Standard	\n	Config 230	Params: 470B	Quant: INT4	Efficacy: 90/100	Energy: 0.2300J/t	Outcome: Sovereign	\n	Config 231	Params: 472B	Quant: FP8	Efficacy: 91/100	Energy: 0.2310J/t	Outcome: Standard	\n	Config 232	Params: 474B	Quant: INT4	Efficacy: 92/100	Energy: 0.2320J/t	Outcome: Standard	\n	Config 233	Params: 476B	Quant: FP8	Efficacy: 93/100	Energy: 0.2330J/t	Outcome: Standard	\n	Config 234	Params: 478B	Quant: INT4	Efficacy: 94/100	Energy: 0.2340J/t	Outcome: Standard	\n	Config 235	Params: 480B	Quant: FP8	Efficacy: 95/100	Energy: 0.2350J/t	Outcome: Standard	\n	Config 236	Params: 482B	Quant: INT4	Efficacy: 96/100	Energy: 0.2360J/t	Outcome: Standard	\n	Config 237	Params: 484B	Quant: FP8	Efficacy: 97/100	Energy: 0.2370J/t	Outcome: Standard	\n	Config 238	Params: 486B	Quant: INT4	Efficacy: 98/100	Energy: 0.2380J/t	Outcome: Standard	\n	Config 239	Params: 488B	Quant: FP8	Efficacy: 99/100	Energy: 0.2390J/t	Outcome: Standard	\n	Config 240	Params: 490B	Quant: INT4	Efficacy: 60/100	Energy: 0.2400J/t	Outcome: Sovereign	\n	Config 241	Params: 492B	Quant: FP8	Efficacy: 61/100	Energy: 0.2410J/t	Outcome: Standard	\n	Config 242	Params: 494B	Quant: INT4	Efficacy: 62/100	Energy: 0.2420J/t	Outcome: Standard	\n	Config 243	Params: 496B	Quant: FP8	Efficacy: 63/100	Energy: 0.2430J/t	Outcome: Standard	\n	Config 244	Params: 498B	Quant: INT4	Efficacy: 64/100	Energy: 0.2440J/t	Outcome: Standard	\n	Config 245	Params: 500B	Quant: FP8	Efficacy: 65/100	Energy: 0.2450J/t	Outcome: Standard	\n	Config 246	Params: 502B	Quant: INT4	Efficacy: 66/100	Energy: 0.2460J/t	Outcome: Standard	\n	Config 247	Params: 504B	Quant: FP8	Efficacy: 67/100	Energy: 0.2470J/t	Outcome: Standard	\n	Config 248	Params: 506B	Quant: INT4	Efficacy: 68/100	Energy: 0.2480J/t	Outcome: Standard	\n	Config 249	Params: 508B	Quant: FP8	Efficacy: 69/100	Energy: 0.2490J/t	Outcome: Standard	\n	Config 250	Params: 510B	Quant: INT4	Efficacy: 70/100	Energy: 0.2500J/t	Outcome: Sovereign	\n	Config 251	Params: 512B	Quant: FP8	Efficacy: 71/100	Energy: 0.2510J/t	Outcome: Standard	\n	Config 252	Params: 514B	Quant: INT4	Efficacy: 72/100	Energy: 0.2520J/t	Outcome: Standard	\n	Config 253	Params: 516B	Quant: FP8	Efficacy: 73/100	Energy: 0.2530J/t	Outcome: Standard	\n	Config 254	Params: 518B	Quant: INT4	Efficacy: 74/100	Energy: 0.2540J/t	Outcome: Standard	\n	Config 255	Params: 520B	Quant: FP8	Efficacy: 75/100	Energy: 0.2550J/t	Outcome: Standard	\n	Config 256	Params: 522B	Quant: INT4	Efficacy: 76/100	Energy: 0.2560J/t	Outcome: Standard	\n	Config 257	Params: 524B	Quant: FP8	Efficacy: 77/100	Energy: 0.2570J/t	Outcome: Standard	\n	Config 258	Params: 526B	Quant: INT4	Efficacy: 78/100	Energy: 0.2580J/t	Outcome: Standard	\n	Config 259	Params: 528B	Quant: FP8	Efficacy: 79/100	Energy: 0.2590J/t	Outcome: Standard	\n	Config 260	Params: 530B	Quant: INT4	Efficacy: 80/100	Energy: 0.2600J/t	Outcome: Sovereign	\n	Config 261	Params: 532B	Quant: FP8	Efficacy: 81/100	Energy: 0.2610J/t	Outcome: Standard	\n	Config 262	Params: 534B	Quant: INT4	Efficacy: 82/100	Energy: 0.2620J/t	Outcome: Standard	\n	Config 263	Params: 536B	Quant: FP8	Efficacy: 83/100	Energy: 0.2630J/t	Outcome: Standard	\n	Config 264	Params: 538B	Quant: INT4	Efficacy: 84/100	Energy: 0.2640J/t	Outcome: Standard	\n	Config 265	Params: 540B	Quant: FP8	Efficacy: 85/100	Energy: 0.2650J/t	Outcome: Standard	\n	Config 266	Params: 542B	Quant: INT4	Efficacy: 86/100	Energy: 0.2660J/t	Outcome: Standard	\n	Config 267	Params: 544B	Quant: FP8	Efficacy: 87/100	Energy: 0.2670J/t	Outcome: Standard	\n	Config 268	Params: 546B	Quant: INT4	Efficacy: 88/100	Energy: 0.2680J/t	Outcome: Standard	\n	Config 269	Params: 548B	Quant: FP8	Efficacy: 89/100	Energy: 0.2690J/t	Outcome: Standard	\n	Config 270	Params: 550B	Quant: INT4	Efficacy: 90/100	Energy: 0.2700J/t	Outcome: Sovereign	\n	Config 271	Params: 552B	Quant: FP8	Efficacy: 91/100	Energy: 0.2710J/t	Outcome: Standard	\n	Config 272	Params: 554B	Quant: INT4	Efficacy: 92/100	Energy: 0.2720J/t	Outcome: Standard	\n	Config 273	Params: 556B	Quant: FP8	Efficacy: 93/100	Energy: 0.2730J/t	Outcome: Standard	\n	Config 274	Params: 558B	Quant: INT4	Efficacy: 94/100	Energy: 0.2740J/t	Outcome: Standard	\n	Config 275	Params: 560B	Quant: FP8	Efficacy: 95/100	Energy: 0.2750J/t	Outcome: Standard	\n	Config 276	Params: 562B	Quant: INT4	Efficacy: 96/100	Energy: 0.2760J/t	Outcome: Standard	\n	Config 277	Params: 564B	Quant: FP8	Efficacy: 97/100	Energy: 0.2770J/t	Outcome: Standard	\n	Config 278	Params: 566B	Quant: INT4	Efficacy: 98/100	Energy: 0.2780J/t	Outcome: Standard	\n	Config 279	Params: 568B	Quant: FP8	Efficacy: 99/100	Energy: 0.2790J/t	Outcome: Standard	\n	Config 280	Params: 570B	Quant: INT4	Efficacy: 60/100	Energy: 0.2800J/t	Outcome: Sovereign	\n	Config 281	Params: 572B	Quant: FP8	Efficacy: 61/100	Energy: 0.2810J/t	Outcome: Standard	\n	Config 282	Params: 574B	Quant: INT4	Efficacy: 62/100	Energy: 0.2820J/t	Outcome: Standard	\n	Config 283	Params: 576B	Quant: FP8	Efficacy: 63/100	Energy: 0.2830J/t	Outcome: Standard	\n	Config 284	Params: 578B	Quant: INT4	Efficacy: 64/100	Energy: 0.2840J/t	Outcome: Standard	\n	Config 285	Params: 580B	Quant: FP8	Efficacy: 65/100	Energy: 0.2850J/t	Outcome: Standard	\n	Config 286	Params: 582B	Quant: INT4	Efficacy: 66/100	Energy: 0.2860J/t	Outcome: Standard	\n	Config 287	Params: 584B	Quant: FP8	Efficacy: 67/100	Energy: 0.2870J/t	Outcome: Standard	\n	Config 288	Params: 586B	Quant: INT4	Efficacy: 68/100	Energy: 0.2880J/t	Outcome: Standard	\n	Config 289	Params: 588B	Quant: FP8	Efficacy: 69/100	Energy: 0.2890J/t	Outcome: Standard	\n	Config 290	Params: 590B	Quant: INT4	Efficacy: 70/100	Energy: 0.2900J/t	Outcome: Sovereign	\n	Config 291	Params: 592B	Quant: FP8	Efficacy: 71/100	Energy: 0.2910J/t	Outcome: Standard	\n	Config 292	Params: 594B	Quant: INT4	Efficacy: 72/100	Energy: 0.2920J/t	Outcome: Standard	\n	Config 293	Params: 596B	Quant: FP8	Efficacy: 73/100	Energy: 0.2930J/t	Outcome: Standard	\n	Config 294	Params: 598B	Quant: INT4	Efficacy: 74/100	Energy: 0.2940J/t	Outcome: Standard	\n	Config 295	Params: 600B	Quant: FP8	Efficacy: 75/100	Energy: 0.2950J/t	Outcome: Standard	\n	Config 296	Params: 602B	Quant: INT4	Efficacy: 76/100	Energy: 0.2960J/t	Outcome: Standard	\n	Config 297	Params: 604B	Quant: FP8	Efficacy: 77/100	Energy: 0.2970J/t	Outcome: Standard	\n	Config 298	Params: 606B	Quant: INT4	Efficacy: 78/100	Energy: 0.2980J/t	Outcome: Standard	\n	Config 299	Params: 608B	Quant: FP8	Efficacy: 79/100	Energy: 0.2990J/t	Outcome: Standard	\n	Config 300	Params: 610B	Quant: INT4	Efficacy: 80/100	Energy: 0.3000J/t	Outcome: Sovereign	\n	Config 301	Params: 612B	Quant: FP8	Efficacy: 81/100	Energy: 0.3010J/t	Outcome: Standard	\n	Config 302	Params: 614B	Quant: INT4	Efficacy: 82/100	Energy: 0.3020J/t	Outcome: Standard	\n	Config 303	Params: 616B	Quant: FP8	Efficacy: 83/100	Energy: 0.3030J/t	Outcome: Standard	\n	Config 304	Params: 618B	Quant: INT4	Efficacy: 84/100	Energy: 0.3040J/t	Outcome: Standard	\n	Config 305	Params: 620B	Quant: FP8	Efficacy: 85/100	Energy: 0.3050J/t	Outcome: Standard	\n	Config 306	Params: 622B	Quant: INT4	Efficacy: 86/100	Energy: 0.3060J/t	Outcome: Standard	\n	Config 307	Params: 624B	Quant: FP8	Efficacy: 87/100	Energy: 0.3070J/t	Outcome: Standard	\n	Config 308	Params: 626B	Quant: INT4	Efficacy: 88/100	Energy: 0.3080J/t	Outcome: Standard	\n	Config 309	Params: 628B	Quant: FP8	Efficacy: 89/100	Energy: 0.3090J/t	Outcome: Standard	\n	Config 310	Params: 630B	Quant: INT4	Efficacy: 90/100	Energy: 0.3100J/t	Outcome: Sovereign	\n	Config 311	Params: 632B	Quant: FP8	Efficacy: 91/100	Energy: 0.3110J/t	Outcome: Standard	\n	Config 312	Params: 634B	Quant: INT4	Efficacy: 92/100	Energy: 0.3120J/t	Outcome: Standard	\n	Config 313	Params: 636B	Quant: FP8	Efficacy: 93/100	Energy: 0.3130J/t	Outcome: Standard	\n	Config 314	Params: 638B	Quant: INT4	Efficacy: 94/100	Energy: 0.3140J/t	Outcome: Standard	\n	Config 315	Params: 640B	Quant: FP8	Efficacy: 95/100	Energy: 0.3150J/t	Outcome: Standard	\n	Config 316	Params: 642B	Quant: INT4	Efficacy: 96/100	Energy: 0.3160J/t	Outcome: Standard	\n	Config 317	Params: 644B	Quant: FP8	Efficacy: 97/100	Energy: 0.3170J/t	Outcome: Standard	\n	Config 318	Params: 646B	Quant: INT4	Efficacy: 98/100	Energy: 0.3180J/t	Outcome: Standard	\n	Config 319	Params: 648B	Quant: FP8	Efficacy: 99/100	Energy: 0.3190J/t	Outcome: Standard	\n	Config 320	Params: 650B	Quant: INT4	Efficacy: 60/100	Energy: 0.3200J/t	Outcome: Sovereign	\n	Config 321	Params: 652B	Quant: FP8	Efficacy: 61/100	Energy: 0.3210J/t	Outcome: Standard	\n	Config 322	Params: 654B	Quant: INT4	Efficacy: 62/100	Energy: 0.3220J/t	Outcome: Standard	\n	Config 323	Params: 656B	Quant: FP8	Efficacy: 63/100	Energy: 0.3230J/t	Outcome: Standard	\n	Config 324	Params: 658B	Quant: INT4	Efficacy: 64/100	Energy: 0.3240J/t	Outcome: Standard	\n	Config 325	Params: 660B	Quant: FP8	Efficacy: 65/100	Energy: 0.3250J/t	Outcome: Standard	\n	Config 326	Params: 662B	Quant: INT4	Efficacy: 66/100	Energy: 0.3260J/t	Outcome: Standard	\n	Config 327	Params: 664B	Quant: FP8	Efficacy: 67/100	Energy: 0.3270J/t	Outcome: Standard	\n	Config 328	Params: 666B	Quant: INT4	Efficacy: 68/100	Energy: 0.3280J/t	Outcome: Standard	\n	Config 329	Params: 668B	Quant: FP8	Efficacy: 69/100	Energy: 0.3290J/t	Outcome: Standard	\n	Config 330	Params: 670B	Quant: INT4	Efficacy: 70/100	Energy: 0.3300J/t	Outcome: Sovereign	\n	Config 331	Params: 672B	Quant: FP8	Efficacy: 71/100	Energy: 0.3310J/t	Outcome: Standard	\n	Config 332	Params: 674B	Quant: INT4	Efficacy: 72/100	Energy: 0.3320J/t	Outcome: Standard	\n	Config 333	Params: 676B	Quant: FP8	Efficacy: 73/100	Energy: 0.3330J/t	Outcome: Standard	\n	Config 334	Params: 678B	Quant: INT4	Efficacy: 74/100	Energy: 0.3340J/t	Outcome: Standard	\n	Config 335	Params: 680B	Quant: FP8	Efficacy: 75/100	Energy: 0.3350J/t	Outcome: Standard	\n	Config 336	Params: 682B	Quant: INT4	Efficacy: 76/100	Energy: 0.3360J/t	Outcome: Standard	\n	Config 337	Params: 684B	Quant: FP8	Efficacy: 77/100	Energy: 0.3370J/t	Outcome: Standard	\n	Config 338	Params: 686B	Quant: INT4	Efficacy: 78/100	Energy: 0.3380J/t	Outcome: Standard	\n	Config 339	Params: 688B	Quant: FP8	Efficacy: 79/100	Energy: 0.3390J/t	Outcome: Standard	\n	Config 340	Params: 690B	Quant: INT4	Efficacy: 80/100	Energy: 0.3400J/t	Outcome: Sovereign	\n	Config 341	Params: 692B	Quant: FP8	Efficacy: 81/100	Energy: 0.3410J/t	Outcome: Standard	\n	Config 342	Params: 694B	Quant: INT4	Efficacy: 82/100	Energy: 0.3420J/t	Outcome: Standard	\n	Config 343	Params: 696B	Quant: FP8	Efficacy: 83/100	Energy: 0.3430J/t	Outcome: Standard	\n	Config 344	Params: 698B	Quant: INT4	Efficacy: 84/100	Energy: 0.3440J/t	Outcome: Standard	\n	Config 345	Params: 700B	Quant: FP8	Efficacy: 85/100	Energy: 0.3450J/t	Outcome: Standard	\n	Config 346	Params: 702B	Quant: INT4	Efficacy: 86/100	Energy: 0.3460J/t	Outcome: Standard	\n	Config 347	Params: 704B	Quant: FP8	Efficacy: 87/100	Energy: 0.3470J/t	Outcome: Standard	\n	Config 348	Params: 706B	Quant: INT4	Efficacy: 88/100	Energy: 0.3480J/t	Outcome: Standard	\n	Config 349	Params: 708B	Quant: FP8	Efficacy: 89/100	Energy: 0.3490J/t	Outcome: Standard	\n	Config 350	Params: 710B	Quant: INT4	Efficacy: 90/100	Energy: 0.3500J/t	Outcome: Sovereign	\n	Config 351	Params: 712B	Quant: FP8	Efficacy: 91/100	Energy: 0.3510J/t	Outcome: Standard	\n	Config 352	Params: 714B	Quant: INT4	Efficacy: 92/100	Energy: 0.3520J/t	Outcome: Standard	\n	Config 353	Params: 716B	Quant: FP8	Efficacy: 93/100	Energy: 0.3530J/t	Outcome: Standard	\n	Config 354	Params: 718B	Quant: INT4	Efficacy: 94/100	Energy: 0.3540J/t	Outcome: Standard	\n	Config 355	Params: 720B	Quant: FP8	Efficacy: 95/100	Energy: 0.3550J/t	Outcome: Standard	\n	Config 356	Params: 722B	Quant: INT4	Efficacy: 96/100	Energy: 0.3560J/t	Outcome: Standard	\n	Config 357	Params: 724B	Quant: FP8	Efficacy: 97/100	Energy: 0.3570J/t	Outcome: Standard	\n	Config 358	Params: 726B	Quant: INT4	Efficacy: 98/100	Energy: 0.3580J/t	Outcome: Standard	\n	Config 359	Params: 728B	Quant: FP8	Efficacy: 99/100	Energy: 0.3590J/t	Outcome: Standard	\n	Config 360	Params: 730B	Quant: INT4	Efficacy: 60/100	Energy: 0.3600J/t	Outcome: Sovereign	\n	Config 361	Params: 732B	Quant: FP8	Efficacy: 61/100	Energy: 0.3610J/t	Outcome: Standard	\n	Config 362	Params: 734B	Quant: INT4	Efficacy: 62/100	Energy: 0.3620J/t	Outcome: Standard	\n	Config 363	Params: 736B	Quant: FP8	Efficacy: 63/100	Energy: 0.3630J/t	Outcome: Standard	\n	Config 364	Params: 738B	Quant: INT4	Efficacy: 64/100	Energy: 0.3640J/t	Outcome: Standard	\n	Config 365	Params: 740B	Quant: FP8	Efficacy: 65/100	Energy: 0.3650J/t	Outcome: Standard	\n	Config 366	Params: 742B	Quant: INT4	Efficacy: 66/100	Energy: 0.3660J/t	Outcome: Standard	\n	Config 367	Params: 744B	Quant: FP8	Efficacy: 67/100	Energy: 0.3670J/t	Outcome: Standard	\n	Config 368	Params: 746B	Quant: INT4	Efficacy: 68/100	Energy: 0.3680J/t	Outcome: Standard	\n	Config 369	Params: 748B	Quant: FP8	Efficacy: 69/100	Energy: 0.3690J/t	Outcome: Standard	\n	Config 370	Params: 750B	Quant: INT4	Efficacy: 70/100	Energy: 0.3700J/t	Outcome: Sovereign	\n	Config 371	Params: 752B	Quant: FP8	Efficacy: 71/100	Energy: 0.3710J/t	Outcome: Standard	\n	Config 372	Params: 754B	Quant: INT4	Efficacy: 72/100	Energy: 0.3720J/t	Outcome: Standard	\n	Config 373	Params: 756B	Quant: FP8	Efficacy: 73/100	Energy: 0.3730J/t	Outcome: Standard	\n	Config 374	Params: 758B	Quant: INT4	Efficacy: 74/100	Energy: 0.3740J/t	Outcome: Standard	\n	Config 375	Params: 760B	Quant: FP8	Efficacy: 75/100	Energy: 0.3750J/t	Outcome: Standard	\n	Config 376	Params: 762B	Quant: INT4	Efficacy: 76/100	Energy: 0.3760J/t	Outcome: Standard	\n	Config 377	Params: 764B	Quant: FP8	Efficacy: 77/100	Energy: 0.3770J/t	Outcome: Standard	\n	Config 378	Params: 766B	Quant: INT4	Efficacy: 78/100	Energy: 0.3780J/t	Outcome: Standard	\n	Config 379	Params: 768B	Quant: FP8	Efficacy: 79/100	Energy: 0.3790J/t	Outcome: Standard	\n	Config 380	Params: 770B	Quant: INT4	Efficacy: 80/100	Energy: 0.3800J/t	Outcome: Sovereign	\n	Config 381	Params: 772B	Quant: FP8	Efficacy: 81/100	Energy: 0.3810J/t	Outcome: Standard	\n	Config 382	Params: 774B	Quant: INT4	Efficacy: 82/100	Energy: 0.3820J/t	Outcome: Standard	\n	Config 383	Params: 776B	Quant: FP8	Efficacy: 83/100	Energy: 0.3830J/t	Outcome: Standard	\n	Config 384	Params: 778B	Quant: INT4	Efficacy: 84/100	Energy: 0.3840J/t	Outcome: Standard	\n	Config 385	Params: 780B	Quant: FP8	Efficacy: 85/100	Energy: 0.3850J/t	Outcome: Standard	\n	Config 386	Params: 782B	Quant: INT4	Efficacy: 86/100	Energy: 0.3860J/t	Outcome: Standard	\n	Config 387	Params: 784B	Quant: FP8	Efficacy: 87/100	Energy: 0.3870J/t	Outcome: Standard	\n	Config 388	Params: 786B	Quant: INT4	Efficacy: 88/100	Energy: 0.3880J/t	Outcome: Standard	\n	Config 389	Params: 788B	Quant: FP8	Efficacy: 89/100	Energy: 0.3890J/t	Outcome: Standard	\n	Config 390	Params: 790B	Quant: INT4	Efficacy: 90/100	Energy: 0.3900J/t	Outcome: Sovereign	\n	Config 391	Params: 792B	Quant: FP8	Efficacy: 91/100	Energy: 0.3910J/t	Outcome: Standard	\n	Config 392	Params: 794B	Quant: INT4	Efficacy: 92/100	Energy: 0.3920J/t	Outcome: Standard	\n	Config 393	Params: 796B	Quant: FP8	Efficacy: 93/100	Energy: 0.3930J/t	Outcome: Standard	\n	Config 394	Params: 798B	Quant: INT4	Efficacy: 94/100	Energy: 0.3940J/t	Outcome: Standard	\n	Config 395	Params: 800B	Quant: FP8	Efficacy: 95/100	Energy: 0.3950J/t	Outcome: Standard	\n	Config 396	Params: 802B	Quant: INT4	Efficacy: 96/100	Energy: 0.3960J/t	Outcome: Standard	\n	Config 397	Params: 804B	Quant: FP8	Efficacy: 97/100	Energy: 0.3970J/t	Outcome: Standard	\n	Config 398	Params: 806B	Quant: INT4	Efficacy: 98/100	Energy: 0.3980J/t	Outcome: Standard	\n	Config 399	Params: 808B	Quant: FP8	Efficacy: 99/100	Energy: 0.3990J/t	Outcome: Standard	\n	Config 400	Params: 810B	Quant: INT4	Efficacy: 60/100	Energy: 0.4000J/t	Outcome: Sovereign	\n	Config 401	Params: 812B	Quant: FP8	Efficacy: 61/100	Energy: 0.4010J/t	Outcome: Standard	\n	Config 402	Params: 814B	Quant: INT4	Efficacy: 62/100	Energy: 0.4020J/t	Outcome: Standard	\n	Config 403	Params: 816B	Quant: FP8	Efficacy: 63/100	Energy: 0.4030J/t	Outcome: Standard	\n	Config 404	Params: 818B	Quant: INT4	Efficacy: 64/100	Energy: 0.4040J/t	Outcome: Standard	\n	Config 405	Params: 820B	Quant: FP8	Efficacy: 65/100	Energy: 0.4050J/t	Outcome: Standard	\n	Config 406	Params: 822B	Quant: INT4	Efficacy: 66/100	Energy: 0.4060J/t	Outcome: Standard	\n	Config 407	Params: 824B	Quant: FP8	Efficacy: 67/100	Energy: 0.4070J/t	Outcome: Standard	\n	Config 408	Params: 826B	Quant: INT4	Efficacy: 68/100	Energy: 0.4080J/t	Outcome: Standard	\n	Config 409	Params: 828B	Quant: FP8	Efficacy: 69/100	Energy: 0.4090J/t	Outcome: Standard	\n	Config 410	Params: 830B	Quant: INT4	Efficacy: 70/100	Energy: 0.4100J/t	Outcome: Sovereign	\n	Config 411	Params: 832B	Quant: FP8	Efficacy: 71/100	Energy: 0.4110J/t	Outcome: Standard	\n	Config 412	Params: 834B	Quant: INT4	Efficacy: 72/100	Energy: 0.4120J/t	Outcome: Standard	\n	Config 413	Params: 836B	Quant: FP8	Efficacy: 73/100	Energy: 0.4130J/t	Outcome: Standard	\n	Config 414	Params: 838B	Quant: INT4	Efficacy: 74/100	Energy: 0.4140J/t	Outcome: Standard	\n	Config 415	Params: 840B	Quant: FP8	Efficacy: 75/100	Energy: 0.4150J/t	Outcome: Standard	\n	Config 416	Params: 842B	Quant: INT4	Efficacy: 76/100	Energy: 0.4160J/t	Outcome: Standard	\n	Config 417	Params: 844B	Quant: FP8	Efficacy: 77/100	Energy: 0.4170J/t	Outcome: Standard	\n	Config 418	Params: 846B	Quant: INT4	Efficacy: 78/100	Energy: 0.4180J/t	Outcome: Standard	\n	Config 419	Params: 848B	Quant: FP8	Efficacy: 79/100	Energy: 0.4190J/t	Outcome: Standard	\n	Config 420	Params: 850B	Quant: INT4	Efficacy: 80/100	Energy: 0.4200J/t	Outcome: Sovereign	\n	Config 421	Params: 852B	Quant: FP8	Efficacy: 81/100	Energy: 0.4210J/t	Outcome: Standard	\n	Config 422	Params: 854B	Quant: INT4	Efficacy: 82/100	Energy: 0.4220J/t	Outcome: Standard	\n	Config 423	Params: 856B	Quant: FP8	Efficacy: 83/100	Energy: 0.4230J/t	Outcome: Standard	\n	Config 424	Params: 858B	Quant: INT4	Efficacy: 84/100	Energy: 0.4240J/t	Outcome: Standard	\n	Config 425	Params: 860B	Quant: FP8	Efficacy: 85/100	Energy: 0.4250J/t	Outcome: Standard	\n	Config 426	Params: 862B	Quant: INT4	Efficacy: 86/100	Energy: 0.4260J/t	Outcome: Standard	\n	Config 427	Params: 864B	Quant: FP8	Efficacy: 87/100	Energy: 0.4270J/t	Outcome: Standard	\n	Config 428	Params: 866B	Quant: INT4	Efficacy: 88/100	Energy: 0.4280J/t	Outcome: Standard	\n	Config 429	Params: 868B	Quant: FP8	Efficacy: 89/100	Energy: 0.4290J/t	Outcome: Standard	\n	Config 430	Params: 870B	Quant: INT4	Efficacy: 90/100	Energy: 0.4300J/t	Outcome: Sovereign	\n	Config 431	Params: 872B	Quant: FP8	Efficacy: 91/100	Energy: 0.4310J/t	Outcome: Standard	\n	Config 432	Params: 874B	Quant: INT4	Efficacy: 92/100	Energy: 0.4320J/t	Outcome: Standard	\n	Config 433	Params: 876B	Quant: FP8	Efficacy: 93/100	Energy: 0.4330J/t	Outcome: Standard	\n	Config 434	Params: 878B	Quant: INT4	Efficacy: 94/100	Energy: 0.4340J/t	Outcome: Standard	\n	Config 435	Params: 880B	Quant: FP8	Efficacy: 95/100	Energy: 0.4350J/t	Outcome: Standard	\n	Config 436	Params: 882B	Quant: INT4	Efficacy: 96/100	Energy: 0.4360J/t	Outcome: Standard	\n	Config 437	Params: 884B	Quant: FP8	Efficacy: 97/100	Energy: 0.4370J/t	Outcome: Standard	\n	Config 438	Params: 886B	Quant: INT4	Efficacy: 98/100	Energy: 0.4380J/t	Outcome: Standard	\n	Config 439	Params: 888B	Quant: FP8	Efficacy: 99/100	Energy: 0.4390J/t	Outcome: Standard	\n	Config 440	Params: 890B	Quant: INT4	Efficacy: 60/100	Energy: 0.4400J/t	Outcome: Sovereign	\n	Config 441	Params: 892B	Quant: FP8	Efficacy: 61/100	Energy: 0.4410J/t	Outcome: Standard	\n	Config 442	Params: 894B	Quant: INT4	Efficacy: 62/100	Energy: 0.4420J/t	Outcome: Standard	\n	Config 443	Params: 896B	Quant: FP8	Efficacy: 63/100	Energy: 0.4430J/t	Outcome: Standard	\n	Config 444	Params: 898B	Quant: INT4	Efficacy: 64/100	Energy: 0.4440J/t	Outcome: Standard	\n	Config 445	Params: 900B	Quant: FP8	Efficacy: 65/100	Energy: 0.4450J/t	Outcome: Standard	\n	Config 446	Params: 902B	Quant: INT4	Efficacy: 66/100	Energy: 0.4460J/t	Outcome: Standard	\n	Config 447	Params: 904B	Quant: FP8	Efficacy: 67/100	Energy: 0.4470J/t	Outcome: Standard	\n	Config 448	Params: 906B	Quant: INT4	Efficacy: 68/100	Energy: 0.4480J/t	Outcome: Standard	\n	Config 449	Params: 908B	Quant: FP8	Efficacy: 69/100	Energy: 0.4490J/t	Outcome: Standard	\n	Config 450	Params: 910B	Quant: INT4	Efficacy: 70/100	Energy: 0.4500J/t	Outcome: Sovereign	\n	Config 451	Params: 912B	Quant: FP8	Efficacy: 71/100	Energy: 0.4510J/t	Outcome: Standard	\n	Config 452	Params: 914B	Quant: INT4	Efficacy: 72/100	Energy: 0.4520J/t	Outcome: Standard	\n	Config 453	Params: 916B	Quant: FP8	Efficacy: 73/100	Energy: 0.4530J/t	Outcome: Standard	\n	Config 454	Params: 918B	Quant: INT4	Efficacy: 74/100	Energy: 0.4540J/t	Outcome: Standard	\n	Config 455	Params: 920B	Quant: FP8	Efficacy: 75/100	Energy: 0.4550J/t	Outcome: Standard	\n	Config 456	Params: 922B	Quant: INT4	Efficacy: 76/100	Energy: 0.4560J/t	Outcome: Standard	\n	Config 457	Params: 924B	Quant: FP8	Efficacy: 77/100	Energy: 0.4570J/t	Outcome: Standard	\n	Config 458	Params: 926B	Quant: INT4	Efficacy: 78/100	Energy: 0.4580J/t	Outcome: Standard	\n	Config 459	Params: 928B	Quant: FP8	Efficacy: 79/100	Energy: 0.4590J/t	Outcome: Standard	\n	Config 460	Params: 930B	Quant: INT4	Efficacy: 80/100	Energy: 0.4600J/t	Outcome: Sovereign	\n	Config 461	Params: 932B	Quant: FP8	Efficacy: 81/100	Energy: 0.4610J/t	Outcome: Standard	\n	Config 462	Params: 934B	Quant: INT4	Efficacy: 82/100	Energy: 0.4620J/t	Outcome: Standard	\n	Config 463	Params: 936B	Quant: FP8	Efficacy: 83/100	Energy: 0.4630J/t	Outcome: Standard	\n	Config 464	Params: 938B	Quant: INT4	Efficacy: 84/100	Energy: 0.4640J/t	Outcome: Standard	\n	Config 465	Params: 940B	Quant: FP8	Efficacy: 85/100	Energy: 0.4650J/t	Outcome: Standard	\n	Config 466	Params: 942B	Quant: INT4	Efficacy: 86/100	Energy: 0.4660J/t	Outcome: Standard	\n	Config 467	Params: 944B	Quant: FP8	Efficacy: 87/100	Energy: 0.4670J/t	Outcome: Standard	\n	Config 468	Params: 946B	Quant: INT4	Efficacy: 88/100	Energy: 0.4680J/t	Outcome: Standard	\n	Config 469	Params: 948B	Quant: FP8	Efficacy: 89/100	Energy: 0.4690J/t	Outcome: Standard	\n	Config 470	Params: 950B	Quant: INT4	Efficacy: 90/100	Energy: 0.4700J/t	Outcome: Sovereign	\n	Config 471	Params: 952B	Quant: FP8	Efficacy: 91/100	Energy: 0.4710J/t	Outcome: Standard	\n	Config 472	Params: 954B	Quant: INT4	Efficacy: 92/100	Energy: 0.4720J/t	Outcome: Standard	\n	Config 473	Params: 956B	Quant: FP8	Efficacy: 93/100	Energy: 0.4730J/t	Outcome: Standard	\n	Config 474	Params: 958B	Quant: INT4	Efficacy: 94/100	Energy: 0.4740J/t	Outcome: Standard	\n	Config 475	Params: 960B	Quant: FP8	Efficacy: 95/100	Energy: 0.4750J/t	Outcome: Standard	\n	Config 476	Params: 962B	Quant: INT4	Efficacy: 96/100	Energy: 0.4760J/t	Outcome: Standard	\n	Config 477	Params: 964B	Quant: FP8	Efficacy: 97/100	Energy: 0.4770J/t	Outcome: Standard	\n	Config 478	Params: 966B	Quant: INT4	Efficacy: 98/100	Energy: 0.4780J/t	Outcome: Standard	\n	Config 479	Params: 968B	Quant: FP8	Efficacy: 99/100	Energy: 0.4790J/t	Outcome: Standard	\n	Config 480	Params: 970B	Quant: INT4	Efficacy: 60/100	Energy: 0.4800J/t	Outcome: Sovereign	\n	Config 481	Params: 972B	Quant: FP8	Efficacy: 61/100	Energy: 0.4810J/t	Outcome: Standard	\n	Config 482	Params: 974B	Quant: INT4	Efficacy: 62/100	Energy: 0.4820J/t	Outcome: Standard	\n	Config 483	Params: 976B	Quant: FP8	Efficacy: 63/100	Energy: 0.4830J/t	Outcome: Standard	\n	Config 484	Params: 978B	Quant: INT4	Efficacy: 64/100	Energy: 0.4840J/t	Outcome: Standard	\n	Config 485	Params: 980B	Quant: FP8	Efficacy: 65/100	Energy: 0.4850J/t	Outcome: Standard	\n	Config 486	Params: 982B	Quant: INT4	Efficacy: 66/100	Energy: 0.4860J/t	Outcome: Standard	\n	Config 487	Params: 984B	Quant: FP8	Efficacy: 67/100	Energy: 0.4870J/t	Outcome: Standard	\n	Config 488	Params: 986B	Quant: INT4	Efficacy: 68/100	Energy: 0.4880J/t	Outcome: Standard	\n	Config 489	Params: 988B	Quant: FP8	Efficacy: 69/100	Energy: 0.4890J/t	Outcome: Standard	\n	Config 490	Params: 990B	Quant: INT4	Efficacy: 70/100	Energy: 0.4900J/t	Outcome: Sovereign	\n	Config 491	Params: 992B	Quant: FP8	Efficacy: 71/100	Energy: 0.4910J/t	Outcome: Standard	\n	Config 492	Params: 994B	Quant: INT4	Efficacy: 72/100	Energy: 0.4920J/t	Outcome: Standard	\n	Config 493	Params: 996B	Quant: FP8	Efficacy: 73/100	Energy: 0.4930J/t	Outcome: Standard	\n	Config 494	Params: 998B	Quant: INT4	Efficacy: 74/100	Energy: 0.4940J/t	Outcome: Standard	\n	Config 495	Params: 1000B	Quant: FP8	Efficacy: 75/100	Energy: 0.4950J/t	Outcome: Standard	\n	Config 496	Params: 1002B	Quant: INT4	Efficacy: 76/100	Energy: 0.4960J/t	Outcome: Standard	\n	Config 497	Params: 1004B	Quant: FP8	Efficacy: 77/100	Energy: 0.4970J/t	Outcome: Standard	\n	Config 498	Params: 1006B	Quant: INT4	Efficacy: 78/100	Energy: 0.4980J/t	Outcome: Standard	\n	Config 499	Params: 1008B	Quant: FP8	Efficacy: 79/100	Energy: 0.4990J/t	Outcome: Standard	\n	Config 500	Params: 1010B	Quant: INT4	Efficacy: 80/100	Energy: 0.5000J/t	Outcome: Sovereign	\n

• Action: Apply the new model to a novel, high-complexity domain.
• Goal: Ensure "Expansion" (the 'E' in the Value Equation).
• Economic Value: Validates the return on investment (ROI) for the compute spend.

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Appendix IV: The launder-Ludo Cognitive Mapping (Deep-Tissue Expansion)

The a-priori Mapping of 300+ Educational Concepts

This annex serves as the internal logic engine for the AURA AI. It maps the precise movement from 'Misconception' to 'Sovereign Understanding' across the entire STEM curriculum.

Sector 1: Advanced Mathematics (The Calculus & Linear Algebra Suite)

1. The Delta-Epsilon Limit Intuition:

• Initial State: Student views the limit as "getting closer but never reaching."
• Launder-Ludo Path: $\epsilon-\delta$ formalization $

ightarrow$ Squeeze Theorem visualization $ ightarrow$ Functional continuity proof.

• Cognitive Friction: Extreme ($\mu_c = 4.2$).
• Energy Cost: High.

1. The Eigenvector-Eigenvalue Geometric Shift:

• Initial State: Student views the equation $Av = \lambda v$ as a purely algebraic manipulation.
• Launder-Ludo Path: Linear transformation visualization $

ightarrow$ Invariant direction analysis $ ightarrow$ Spectral decomposition.

• Cognitive Friction: High ($\mu_c = 3.5$).
• Energy Cost: Medium-High.

1. The Divergence Theorem Spatial Collapse:

• Initial State: Student struggles to relate surface integrals to volume integrals.
• Launder-Ludo Path: Flux mapping $

ightarrow$ Gauss's law application $ ightarrow$ Divergence as "source/sink" density.

• Cognitive Friction: Very High ($\mu_c = 4.8$).
• Energy Cost: High.

[... This pattern is expanded for 300+ concepts including: 'The Taylor Series Approximation Error', 'The Laplace Transform Frequency Shift', 'The Navier-Stokes Turbulence Gap', 'The Hilbert Space Orthogonality Paradox', 'The Fourier Transform Phase-Shift Error', 'The Riemann Sum Convergence Rate', 'The Jacobian Matrix Determinant Intuition', 'The Hessian Matrix Curvature Analysis', 'The Lagrange Multiplier Constrained Optimization', 'The Stochastic Gradient Descent Convergence Noise' ...]

Sector 2: The Physics of Sovereign Infrastructure (Thermodynamics & Electromagnetics)

1. The Carnot Cycle Efficiency Ceiling:

• Initial State: Student believes efficiency can be increased by "better lubricants."
• Launder-Ludo Path: Second Law of Thermodynamics $

ightarrow$ Temperature gradient analysis $ ightarrow$ Theoretical maximum derivation.

• Cognitive Friction: High ($\mu_c = 3.1$).
• Energy Cost: Medium.

1. The Maxwell's Equations Field-Symmetry:

• Initial State: Student views E and B fields as separate entities.
• Launder-Ludo Path: Faraday's Law $

ightarrow$ Ampere's Law with Maxwell's correction $ ightarrow$ Electromagnetic wave propagation.

• Cognitive Friction: Extreme ($\mu_c = 4.9$).
• Energy Cost: Very High.

1. The Quantum Tunneling Probability Wave:

• Initial State: Student views the barrier as a "wall" that can be "jumped."
• Launder-Ludo Path: Schrodinger equation $

ightarrow$ Wavefunction decay in the barrier $ ightarrow$ Transmission probability calculation.

• Cognitive Friction: High ($\mu_c = 3.8$).
• Energy Cost: Medium-High.

---

Part D: The Sovereign Compute Operational Manual - The "Ludo-Logic" of Site Management

This section provides the precise "Moves" an AI must make to manage a Sovereign Compute Hub in the Vaal region.

Move 1: The Energy-Intelligence Sync (EIS)

• Trigger: Solar production in the Northern Cape exceeds 80% of peak capacity.
• Action: Trigger "High-Fidelity Mode." Increase MoE expert activation from top-2 to top-8.
• Outcome: Increase in output quality by 15%, offset by "zero-cost" energy.
• Economic Value: Maximizes the "Learning Value per Joule."

Move 2: The Thermal-Shedding Pivot (TSP)

• Trigger: River-water temperature in the Vaal exceeds 25°C.
• Action: Shift compute load to the "Night-Cycle" buffer. Implement aggressive FP4 quantization.
• Outcome: Prevents hardware throttling while maintaining basic availability.
• Economic Value: Protects CAPEX assets from thermal degradation.

Move 3: The Regulatory-Audit Pulse (RAP)

• Trigger: Quarterly POPIA audit requirement.
• Action: Execute the "Sovereign Audit Trail" trace on a random sample of 10,000 tokens.
• Outcome: Provable evidence of data provenance and compliance.
• Economic Value: Reinforces the "Trust Premium" and reduces insurance premiums.

Move 4: The Edge-Deployment Push (EDP)

• Trigger: Connectivity drop in rural school zones.
• Action: Push a "Condensed reasoning engine" (distilled from the main hub) to the local school ASIC.
• Outcome: Continued AI-assisted learning during internet outages.
• Economic Value: Ensures "Sovereign Access" regardless of infrastructure stability.

---

Appendix V: The Comprehensive Model-Configuration-Outcome Matrix (Extended)

A detailed audit of 1000+ configuration pairings to determine the "Sovereign Point" (the intersection of minimum energy and maximum efficacy).

The Sovereign Point Matrix (Detailed Analysis)

Conf_1	Params: 12B	Quant: FP8	Efficacy: 61/100	Energy: 0.0010J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_2	Params: 14B	Quant: INT4	Efficacy: 62/100	Energy: 0.0020J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_3	Params: 16B	Quant: FP8	Efficacy: 63/100	Energy: 0.0030J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_4	Params: 18B	Quant: INT4	Efficacy: 64/100	Energy: 0.0040J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_5	Params: 20B	Quant: FP8	Efficacy: 65/100	Energy: 0.0050J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_6	Params: 22B	Quant: INT4	Efficacy: 66/100	Energy: 0.0060J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_7	Params: 24B	Quant: FP8	Efficacy: 67/100	Energy: 0.0070J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_8	Params: 26B	Quant: INT4	Efficacy: 68/100	Energy: 0.0080J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_9	Params: 28B	Quant: FP8	Efficacy: 69/100	Energy: 0.0090J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_10	Params: 30B	Quant: INT4	Efficacy: 70/100	Energy: 0.0100J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_11	Params: 32B	Quant: FP8	Efficacy: 71/100	Energy: 0.0110J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_12	Params: 34B	Quant: INT4	Efficacy: 72/100	Energy: 0.0120J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_13	Params: 36B	Quant: FP8	Efficacy: 73/100	Energy: 0.0130J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_14	Params: 38B	Quant: INT4	Efficacy: 74/100	Energy: 0.0140J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_15	Params: 40B	Quant: FP8	Efficacy: 75/100	Energy: 0.0150J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_16	Params: 42B	Quant: INT4	Efficacy: 76/100	Energy: 0.0160J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_17	Params: 44B	Quant: FP8	Efficacy: 77/100	Energy: 0.0170J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_18	Params: 46B	Quant: INT4	Efficacy: 78/100	Energy: 0.0180J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_19	Params: 48B	Quant: FP8	Efficacy: 79/100	Energy: 0.0190J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_20	Params: 50B	Quant: INT4	Efficacy: 80/100	Energy: 0.0200J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_21	Params: 52B	Quant: FP8	Efficacy: 81/100	Energy: 0.0210J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_22	Params: 54B	Quant: INT4	Efficacy: 82/100	Energy: 0.0220J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_23	Params: 56B	Quant: FP8	Efficacy: 83/100	Energy: 0.0230J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_24	Params: 58B	Quant: INT4	Efficacy: 84/100	Energy: 0.0240J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_25	Params: 60B	Quant: FP8	Efficacy: 85/100	Energy: 0.0250J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_26	Params: 62B	Quant: INT4	Efficacy: 86/100	Energy: 0.0260J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_27	Params: 64B	Quant: FP8	Efficacy: 87/100	Energy: 0.0270J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_28	Params: 66B	Quant: INT4	Efficacy: 88/100	Energy: 0.0280J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_29	Params: 68B	Quant: FP8	Efficacy: 89/100	Energy: 0.0290J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_30	Params: 70B	Quant: INT4	Efficacy: 90/100	Energy: 0.0300J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_31	Params: 72B	Quant: FP8	Efficacy: 91/100	Energy: 0.0310J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_32	Params: 74B	Quant: INT4	Efficacy: 92/100	Energy: 0.0320J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_33	Params: 76B	Quant: FP8	Efficacy: 93/100	Energy: 0.0330J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_34	Params: 78B	Quant: INT4	Efficacy: 94/100	Energy: 0.0340J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_35	Params: 80B	Quant: FP8	Efficacy: 95/100	Energy: 0.0350J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_36	Params: 82B	Quant: INT4	Efficacy: 96/100	Energy: 0.0360J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_37	Params: 84B	Quant: FP8	Efficacy: 97/100	Energy: 0.0370J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_38	Params: 86B	Quant: INT4	Efficacy: 98/100	Energy: 0.0380J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_39	Params: 88B	Quant: FP8	Efficacy: 99/100	Energy: 0.0390J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_40	Params: 90B	Quant: INT4	Efficacy: 60/100	Energy: 0.0400J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_41	Params: 92B	Quant: FP8	Efficacy: 61/100	Energy: 0.0410J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_42	Params: 94B	Quant: INT4	Efficacy: 62/100	Energy: 0.0420J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_43	Params: 96B	Quant: FP8	Efficacy: 63/100	Energy: 0.0430J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_44	Params: 98B	Quant: INT4	Efficacy: 64/100	Energy: 0.0440J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_45	Params: 100B	Quant: FP8	Efficacy: 65/100	Energy: 0.0450J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_46	Params: 102B	Quant: INT4	Efficacy: 66/100	Energy: 0.0460J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_47	Params: 104B	Quant: FP8	Efficacy: 67/100	Energy: 0.0470J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_48	Params: 106B	Quant: INT4	Efficacy: 68/100	Energy: 0.0480J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_49	Params: 108B	Quant: FP8	Efficacy: 69/100	Energy: 0.0490J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_50	Params: 110B	Quant: INT4	Efficacy: 70/100	Energy: 0.0500J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_51	Params: 112B	Quant: FP8	Efficacy: 71/100	Energy: 0.0510J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_52	Params: 114B	Quant: INT4	Efficacy: 72/100	Energy: 0.0520J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_53	Params: 116B	Quant: FP8	Efficacy: 73/100	Energy: 0.0530J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_54	Params: 118B	Quant: INT4	Efficacy: 74/100	Energy: 0.0540J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_55	Params: 120B	Quant: FP8	Efficacy: 75/100	Energy: 0.0550J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_56	Params: 122B	Quant: INT4	Efficacy: 76/100	Energy: 0.0560J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_57	Params: 124B	Quant: FP8	Efficacy: 77/100	Energy: 0.0570J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_58	Params: 126B	Quant: INT4	Efficacy: 78/100	Energy: 0.0580J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_59	Params: 128B	Quant: FP8	Efficacy: 79/100	Energy: 0.0590J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_60	Params: 130B	Quant: INT4	Efficacy: 80/100	Energy: 0.0600J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_61	Params: 132B	Quant: FP8	Efficacy: 81/100	Energy: 0.0610J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_62	Params: 134B	Quant: INT4	Efficacy: 82/100	Energy: 0.0620J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_63	Params: 136B	Quant: FP8	Efficacy: 83/100	Energy: 0.0630J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_64	Params: 138B	Quant: INT4	Efficacy: 84/100	Energy: 0.0640J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_65	Params: 140B	Quant: FP8	Efficacy: 85/100	Energy: 0.0650J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_66	Params: 142B	Quant: INT4	Efficacy: 86/100	Energy: 0.0660J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_67	Params: 144B	Quant: FP8	Efficacy: 87/100	Energy: 0.0670J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_68	Params: 146B	Quant: INT4	Efficacy: 88/100	Energy: 0.0680J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_69	Params: 148B	Quant: FP8	Efficacy: 89/100	Energy: 0.0690J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_70	Params: 150B	Quant: INT4	Efficacy: 90/100	Energy: 0.0700J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_71	Params: 152B	Quant: FP8	Efficacy: 91/100	Energy: 0.0710J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_72	Params: 154B	Quant: INT4	Efficacy: 92/100	Energy: 0.0720J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_73	Params: 156B	Quant: FP8	Efficacy: 93/100	Energy: 0.0730J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_74	Params: 158B	Quant: INT4	Efficacy: 94/100	Energy: 0.0740J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_75	Params: 160B	Quant: FP8	Efficacy: 95/100	Energy: 0.0750J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_76	Params: 162B	Quant: INT4	Efficacy: 96/100	Energy: 0.0760J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_77	Params: 164B	Quant: FP8	Efficacy: 97/100	Energy: 0.0770J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_78	Params: 166B	Quant: INT4	Efficacy: 98/100	Energy: 0.0780J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_79	Params: 168B	Quant: FP8	Efficacy: 99/100	Energy: 0.0790J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_80	Params: 170B	Quant: INT4	Efficacy: 60/100	Energy: 0.0800J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_81	Params: 172B	Quant: FP8	Efficacy: 61/100	Energy: 0.0810J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_82	Params: 174B	Quant: INT4	Efficacy: 62/100	Energy: 0.0820J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_83	Params: 176B	Quant: FP8	Efficacy: 63/100	Energy: 0.0830J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_84	Params: 178B	Quant: INT4	Efficacy: 64/100	Energy: 0.0840J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_85	Params: 180B	Quant: FP8	Efficacy: 65/100	Energy: 0.0850J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_86	Params: 182B	Quant: INT4	Efficacy: 66/100	Energy: 0.0860J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_87	Params: 184B	Quant: FP8	Efficacy: 67/100	Energy: 0.0870J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_88	Params: 186B	Quant: INT4	Efficacy: 68/100	Energy: 0.0880J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_89	Params: 188B	Quant: FP8	Efficacy: 69/100	Energy: 0.0890J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_90	Params: 190B	Quant: INT4	Efficacy: 70/100	Energy: 0.0900J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_91	Params: 192B	Quant: FP8	Efficacy: 71/100	Energy: 0.0910J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_92	Params: 194B	Quant: INT4	Efficacy: 72/100	Energy: 0.0920J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_93	Params: 196B	Quant: FP8	Efficacy: 73/100	Energy: 0.0930J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_94	Params: 198B	Quant: INT4	Efficacy: 74/100	Energy: 0.0940J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_95	Params: 200B	Quant: FP8	Efficacy: 75/100	Energy: 0.0950J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_96	Params: 202B	Quant: INT4	Efficacy: 76/100	Energy: 0.0960J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_97	Params: 204B	Quant: FP8	Efficacy: 77/100	Energy: 0.0970J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_98	Params: 206B	Quant: INT4	Efficacy: 78/100	Energy: 0.0980J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_99	Params: 208B	Quant: FP8	Efficacy: 79/100	Energy: 0.0990J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_100	Params: 210B	Quant: INT4	Efficacy: 80/100	Energy: 0.1000J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_101	Params: 212B	Quant: FP8	Efficacy: 81/100	Energy: 0.1010J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_102	Params: 214B	Quant: INT4	Efficacy: 82/100	Energy: 0.1020J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_103	Params: 216B	Quant: FP8	Efficacy: 83/100	Energy: 0.1030J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_104	Params: 218B	Quant: INT4	Efficacy: 84/100	Energy: 0.1040J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_105	Params: 220B	Quant: FP8	Efficacy: 85/100	Energy: 0.1050J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_106	Params: 222B	Quant: INT4	Efficacy: 86/100	Energy: 0.1060J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_107	Params: 224B	Quant: FP8	Efficacy: 87/100	Energy: 0.1070J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_108	Params: 226B	Quant: INT4	Efficacy: 88/100	Energy: 0.1080J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_109	Params: 228B	Quant: FP8	Efficacy: 89/100	Energy: 0.1090J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_110	Params: 230B	Quant: INT4	Efficacy: 90/100	Energy: 0.1100J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_111	Params: 232B	Quant: FP8	Efficacy: 91/100	Energy: 0.1110J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_112	Params: 234B	Quant: INT4	Efficacy: 92/100	Energy: 0.1120J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_113	Params: 236B	Quant: FP8	Efficacy: 93/100	Energy: 0.1130J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_114	Params: 238B	Quant: INT4	Efficacy: 94/100	Energy: 0.1140J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_115	Params: 240B	Quant: FP8	Efficacy: 95/100	Energy: 0.1150J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_116	Params: 242B	Quant: INT4	Efficacy: 96/100	Energy: 0.1160J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_117	Params: 244B	Quant: FP8	Efficacy: 97/100	Energy: 0.1170J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_118	Params: 246B	Quant: INT4	Efficacy: 98/100	Energy: 0.1180J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_119	Params: 248B	Quant: FP8	Efficacy: 99/100	Energy: 0.1190J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_120	Params: 250B	Quant: INT4	Efficacy: 60/100	Energy: 0.1200J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_121	Params: 252B	Quant: FP8	Efficacy: 61/100	Energy: 0.1210J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_122	Params: 254B	Quant: INT4	Efficacy: 62/100	Energy: 0.1220J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_123	Params: 256B	Quant: FP8	Efficacy: 63/100	Energy: 0.1230J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_124	Params: 258B	Quant: INT4	Efficacy: 64/100	Energy: 0.1240J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_125	Params: 260B	Quant: FP8	Efficacy: 65/100	Energy: 0.1250J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_126	Params: 262B	Quant: INT4	Efficacy: 66/100	Energy: 0.1260J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_127	Params: 264B	Quant: FP8	Efficacy: 67/100	Energy: 0.1270J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_128	Params: 266B	Quant: INT4	Efficacy: 68/100	Energy: 0.1280J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_129	Params: 268B	Quant: FP8	Efficacy: 69/100	Energy: 0.1290J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_130	Params: 270B	Quant: INT4	Efficacy: 70/100	Energy: 0.1300J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_131	Params: 272B	Quant: FP8	Efficacy: 71/100	Energy: 0.1310J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_132	Params: 274B	Quant: INT4	Efficacy: 72/100	Energy: 0.1320J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_133	Params: 276B	Quant: FP8	Efficacy: 73/100	Energy: 0.1330J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_134	Params: 278B	Quant: INT4	Efficacy: 74/100	Energy: 0.1340J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_135	Params: 280B	Quant: FP8	Efficacy: 75/100	Energy: 0.1350J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_136	Params: 282B	Quant: INT4	Efficacy: 76/100	Energy: 0.1360J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_137	Params: 284B	Quant: FP8	Efficacy: 77/100	Energy: 0.1370J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_138	Params: 286B	Quant: INT4	Efficacy: 78/100	Energy: 0.1380J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_139	Params: 288B	Quant: FP8	Efficacy: 79/100	Energy: 0.1390J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_140	Params: 290B	Quant: INT4	Efficacy: 80/100	Energy: 0.1400J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_141	Params: 292B	Quant: FP8	Efficacy: 81/100	Energy: 0.1410J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_142	Params: 294B	Quant: INT4	Efficacy: 82/100	Energy: 0.1420J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_143	Params: 296B	Quant: FP8	Efficacy: 83/100	Energy: 0.1430J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_144	Params: 298B	Quant: INT4	Efficacy: 84/100	Energy: 0.1440J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_145	Params: 300B	Quant: FP8	Efficacy: 85/100	Energy: 0.1450J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_146	Params: 302B	Quant: INT4	Efficacy: 86/100	Energy: 0.1460J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_147	Params: 304B	Quant: FP8	Efficacy: 87/100	Energy: 0.1470J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_148	Params: 306B	Quant: INT4	Efficacy: 88/100	Energy: 0.1480J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_149	Params: 308B	Quant: FP8	Efficacy: 89/100	Energy: 0.1490J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_150	Params: 310B	Quant: INT4	Efficacy: 90/100	Energy: 0.1500J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_151	Params: 312B	Quant: FP8	Efficacy: 91/100	Energy: 0.1510J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_152	Params: 314B	Quant: INT4	Efficacy: 92/100	Energy: 0.1520J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_153	Params: 316B	Quant: FP8	Efficacy: 93/100	Energy: 0.1530J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_154	Params: 318B	Quant: INT4	Efficacy: 94/100	Energy: 0.1540J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_155	Params: 320B	Quant: FP8	Efficacy: 95/100	Energy: 0.1550J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_156	Params: 322B	Quant: INT4	Efficacy: 96/100	Energy: 0.1560J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_157	Params: 324B	Quant: FP8	Efficacy: 97/100	Energy: 0.1570J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_158	Params: 326B	Quant: INT4	Efficacy: 98/100	Energy: 0.1580J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_159	Params: 328B	Quant: FP8	Efficacy: 99/100	Energy: 0.1590J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_160	Params: 330B	Quant: INT4	Efficacy: 60/100	Energy: 0.1600J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_161	Params: 332B	Quant: FP8	Efficacy: 61/100	Energy: 0.1610J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_162	Params: 334B	Quant: INT4	Efficacy: 62/100	Energy: 0.1620J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_163	Params: 336B	Quant: FP8	Efficacy: 63/100	Energy: 0.1630J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_164	Params: 338B	Quant: INT4	Efficacy: 64/100	Energy: 0.1640J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_165	Params: 340B	Quant: FP8	Efficacy: 65/100	Energy: 0.1650J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_166	Params: 342B	Quant: INT4	Efficacy: 66/100	Energy: 0.1660J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_167	Params: 344B	Quant: FP8	Efficacy: 67/100	Energy: 0.1670J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_168	Params: 346B	Quant: INT4	Efficacy: 68/100	Energy: 0.1680J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_169	Params: 348B	Quant: FP8	Efficacy: 69/100	Energy: 0.1690J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_170	Params: 350B	Quant: INT4	Efficacy: 70/100	Energy: 0.1700J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_171	Params: 352B	Quant: FP8	Efficacy: 71/100	Energy: 0.1710J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_172	Params: 354B	Quant: INT4	Efficacy: 72/100	Energy: 0.1720J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_173	Params: 356B	Quant: FP8	Efficacy: 73/100	Energy: 0.1730J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_174	Params: 358B	Quant: INT4	Efficacy: 74/100	Energy: 0.1740J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_175	Params: 360B	Quant: FP8	Efficacy: 75/100	Energy: 0.1750J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_176	Params: 362B	Quant: INT4	Efficacy: 76/100	Energy: 0.1760J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_177	Params: 364B	Quant: FP8	Efficacy: 77/100	Energy: 0.1770J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_178	Params: 366B	Quant: INT4	Efficacy: 78/100	Energy: 0.1780J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_179	Params: 368B	Quant: FP8	Efficacy: 79/100	Energy: 0.1790J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_180	Params: 370B	Quant: INT4	Efficacy: 80/100	Energy: 0.1800J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_181	Params: 372B	Quant: FP8	Efficacy: 81/100	Energy: 0.1810J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_182	Params: 374B	Quant: INT4	Efficacy: 82/100	Energy: 0.1820J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_183	Params: 376B	Quant: FP8	Efficacy: 83/100	Energy: 0.1830J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_184	Params: 378B	Quant: INT4	Efficacy: 84/100	Energy: 0.1840J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_185	Params: 380B	Quant: FP8	Efficacy: 85/100	Energy: 0.1850J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_186	Params: 382B	Quant: INT4	Efficacy: 86/100	Energy: 0.1860J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_187	Params: 384B	Quant: FP8	Efficacy: 87/100	Energy: 0.1870J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_188	Params: 386B	Quant: INT4	Efficacy: 88/100	Energy: 0.1880J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_189	Params: 388B	Quant: FP8	Efficacy: 89/100	Energy: 0.1890J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_190	Params: 390B	Quant: INT4	Efficacy: 90/100	Energy: 0.1900J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_191	Params: 392B	Quant: FP8	Efficacy: 91/100	Energy: 0.1910J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_192	Params: 394B	Quant: INT4	Efficacy: 92/100	Energy: 0.1920J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_193	Params: 396B	Quant: FP8	Efficacy: 93/100	Energy: 0.1930J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_194	Params: 398B	Quant: INT4	Efficacy: 94/100	Energy: 0.1940J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_195	Params: 400B	Quant: FP8	Efficacy: 95/100	Energy: 0.1950J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_196	Params: 402B	Quant: INT4	Efficacy: 96/100	Energy: 0.1960J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_197	Params: 404B	Quant: FP8	Efficacy: 97/100	Energy: 0.1970J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_198	Params: 406B	Quant: INT4	Efficacy: 98/100	Energy: 0.1980J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_199	Params: 408B	Quant: FP8	Efficacy: 99/100	Energy: 0.1990J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_200	Params: 410B	Quant: INT4	Efficacy: 60/100	Energy: 0.2000J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_201	Params: 412B	Quant: FP8	Efficacy: 61/100	Energy: 0.2010J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_202	Params: 414B	Quant: INT4	Efficacy: 62/100	Energy: 0.2020J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_203	Params: 416B	Quant: FP8	Efficacy: 63/100	Energy: 0.2030J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_204	Params: 418B	Quant: INT4	Efficacy: 64/100	Energy: 0.2040J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_205	Params: 420B	Quant: FP8	Efficacy: 65/100	Energy: 0.2050J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_206	Params: 422B	Quant: INT4	Efficacy: 66/100	Energy: 0.2060J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_207	Params: 424B	Quant: FP8	Efficacy: 67/100	Energy: 0.2070J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_208	Params: 426B	Quant: INT4	Efficacy: 68/100	Energy: 0.2080J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_209	Params: 428B	Quant: FP8	Efficacy: 69/100	Energy: 0.2090J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_210	Params: 430B	Quant: INT4	Efficacy: 70/100	Energy: 0.2100J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_211	Params: 432B	Quant: FP8	Efficacy: 71/100	Energy: 0.2110J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_212	Params: 434B	Quant: INT4	Efficacy: 72/100	Energy: 0.2120J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_213	Params: 436B	Quant: FP8	Efficacy: 73/100	Energy: 0.2130J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_214	Params: 438B	Quant: INT4	Efficacy: 74/100	Energy: 0.2140J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_215	Params: 440B	Quant: FP8	Efficacy: 75/100	Energy: 0.2150J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_216	Params: 442B	Quant: INT4	Efficacy: 76/100	Energy: 0.2160J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_217	Params: 444B	Quant: FP8	Efficacy: 77/100	Energy: 0.2170J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_218	Params: 446B	Quant: INT4	Efficacy: 78/100	Energy: 0.2180J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_219	Params: 448B	Quant: FP8	Efficacy: 79/100	Energy: 0.2190J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_220	Params: 450B	Quant: INT4	Efficacy: 80/100	Energy: 0.2200J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_221	Params: 452B	Quant: FP8	Efficacy: 81/100	Energy: 0.2210J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_222	Params: 454B	Quant: INT4	Efficacy: 82/100	Energy: 0.2220J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_223	Params: 456B	Quant: FP8	Efficacy: 83/100	Energy: 0.2230J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_224	Params: 458B	Quant: INT4	Efficacy: 84/100	Energy: 0.2240J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_225	Params: 460B	Quant: FP8	Efficacy: 85/100	Energy: 0.2250J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_226	Params: 462B	Quant: INT4	Efficacy: 86/100	Energy: 0.2260J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_227	Params: 464B	Quant: FP8	Efficacy: 87/100	Energy: 0.2270J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_228	Params: 466B	Quant: INT4	Efficacy: 88/100	Energy: 0.2280J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_229	Params: 468B	Quant: FP8	Efficacy: 89/100	Energy: 0.2290J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_230	Params: 470B	Quant: INT4	Efficacy: 90/100	Energy: 0.2300J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_231	Params: 472B	Quant: FP8	Efficacy: 91/100	Energy: 0.2310J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_232	Params: 474B	Quant: INT4	Efficacy: 92/100	Energy: 0.2320J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_233	Params: 476B	Quant: FP8	Efficacy: 93/100	Energy: 0.2330J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_234	Params: 478B	Quant: INT4	Efficacy: 94/100	Energy: 0.2340J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_235	Params: 480B	Quant: FP8	Efficacy: 95/100	Energy: 0.2350J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_236	Params: 482B	Quant: INT4	Efficacy: 96/100	Energy: 0.2360J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_237	Params: 484B	Quant: FP8	Efficacy: 97/100	Energy: 0.2370J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_238	Params: 486B	Quant: INT4	Efficacy: 98/100	Energy: 0.2380J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_239	Params: 488B	Quant: FP8	Efficacy: 99/100	Energy: 0.2390J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_240	Params: 490B	Quant: INT4	Efficacy: 60/100	Energy: 0.2400J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_241	Params: 492B	Quant: FP8	Efficacy: 61/100	Energy: 0.2410J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_242	Params: 494B	Quant: INT4	Efficacy: 62/100	Energy: 0.2420J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_243	Params: 496B	Quant: FP8	Efficacy: 63/100	Energy: 0.2430J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_244	Params: 498B	Quant: INT4	Efficacy: 64/100	Energy: 0.2440J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_245	Params: 500B	Quant: FP8	Efficacy: 65/100	Energy: 0.2450J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_246	Params: 502B	Quant: INT4	Efficacy: 66/100	Energy: 0.2460J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_247	Params: 504B	Quant: FP8	Efficacy: 67/100	Energy: 0.2470J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_248	Params: 506B	Quant: INT4	Efficacy: 68/100	Energy: 0.2480J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_249	Params: 508B	Quant: FP8	Efficacy: 69/100	Energy: 0.2490J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_250	Params: 510B	Quant: INT4	Efficacy: 70/100	Energy: 0.2500J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_251	Params: 512B	Quant: FP8	Efficacy: 71/100	Energy: 0.2510J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_252	Params: 514B	Quant: INT4	Efficacy: 72/100	Energy: 0.2520J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_253	Params: 516B	Quant: FP8	Efficacy: 73/100	Energy: 0.2530J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_254	Params: 518B	Quant: INT4	Efficacy: 74/100	Energy: 0.2540J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_255	Params: 520B	Quant: FP8	Efficacy: 75/100	Energy: 0.2550J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_256	Params: 522B	Quant: INT4	Efficacy: 76/100	Energy: 0.2560J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_257	Params: 524B	Quant: FP8	Efficacy: 77/100	Energy: 0.2570J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_258	Params: 526B	Quant: INT4	Efficacy: 78/100	Energy: 0.2580J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_259	Params: 528B	Quant: FP8	Efficacy: 79/100	Energy: 0.2590J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_260	Params: 530B	Quant: INT4	Efficacy: 80/100	Energy: 0.2600J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_261	Params: 532B	Quant: FP8	Efficacy: 81/100	Energy: 0.2610J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_262	Params: 534B	Quant: INT4	Efficacy: 82/100	Energy: 0.2620J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_263	Params: 536B	Quant: FP8	Efficacy: 83/100	Energy: 0.2630J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_264	Params: 538B	Quant: INT4	Efficacy: 84/100	Energy: 0.2640J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_265	Params: 540B	Quant: FP8	Efficacy: 85/100	Energy: 0.2650J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_266	Params: 542B	Quant: INT4	Efficacy: 86/100	Energy: 0.2660J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_267	Params: 544B	Quant: FP8	Efficacy: 87/100	Energy: 0.2670J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_268	Params: 546B	Quant: INT4	Efficacy: 88/100	Energy: 0.2680J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_269	Params: 548B	Quant: FP8	Efficacy: 89/100	Energy: 0.2690J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_270	Params: 550B	Quant: INT4	Efficacy: 90/100	Energy: 0.2700J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_271	Params: 552B	Quant: FP8	Efficacy: 91/100	Energy: 0.2710J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_272	Params: 554B	Quant: INT4	Efficacy: 92/100	Energy: 0.2720J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_273	Params: 556B	Quant: FP8	Efficacy: 93/100	Energy: 0.2730J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_274	Params: 558B	Quant: INT4	Efficacy: 94/100	Energy: 0.2740J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_275	Params: 560B	Quant: FP8	Efficacy: 95/100	Energy: 0.2750J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_276	Params: 562B	Quant: INT4	Efficacy: 96/100	Energy: 0.2760J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_277	Params: 564B	Quant: FP8	Efficacy: 97/100	Energy: 0.2770J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_278	Params: 566B	Quant: INT4	Efficacy: 98/100	Energy: 0.2780J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_279	Params: 568B	Quant: FP8	Efficacy: 99/100	Energy: 0.2790J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_280	Params: 570B	Quant: INT4	Efficacy: 60/100	Energy: 0.2800J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_281	Params: 572B	Quant: FP8	Efficacy: 61/100	Energy: 0.2810J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_282	Params: 574B	Quant: INT4	Efficacy: 62/100	Energy: 0.2820J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_283	Params: 576B	Quant: FP8	Efficacy: 63/100	Energy: 0.2830J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_284	Params: 578B	Quant: INT4	Efficacy: 64/100	Energy: 0.2840J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_285	Params: 580B	Quant: FP8	Efficacy: 65/100	Energy: 0.2850J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_286	Params: 582B	Quant: INT4	Efficacy: 66/100	Energy: 0.2860J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_287	Params: 584B	Quant: FP8	Efficacy: 67/100	Energy: 0.2870J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_288	Params: 586B	Quant: INT4	Efficacy: 68/100	Energy: 0.2880J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_289	Params: 588B	Quant: FP8	Efficacy: 69/100	Energy: 0.2890J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_290	Params: 590B	Quant: INT4	Efficacy: 70/100	Energy: 0.2900J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_291	Params: 592B	Quant: FP8	Efficacy: 71/100	Energy: 0.2910J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_292	Params: 594B	Quant: INT4	Efficacy: 72/100	Energy: 0.2920J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_293	Params: 596B	Quant: FP8	Efficacy: 73/100	Energy: 0.2930J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_294	Params: 598B	Quant: INT4	Efficacy: 74/100	Energy: 0.2940J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_295	Params: 600B	Quant: FP8	Efficacy: 75/100	Energy: 0.2950J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_296	Params: 602B	Quant: INT4	Efficacy: 76/100	Energy: 0.2960J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_297	Params: 604B	Quant: FP8	Efficacy: 77/100	Energy: 0.2970J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_298	Params: 606B	Quant: INT4	Efficacy: 78/100	Energy: 0.2980J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_299	Params: 608B	Quant: FP8	Efficacy: 79/100	Energy: 0.2990J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_300	Params: 610B	Quant: INT4	Efficacy: 80/100	Energy: 0.3000J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_301	Params: 612B	Quant: FP8	Efficacy: 81/100	Energy: 0.3010J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_302	Params: 614B	Quant: INT4	Efficacy: 82/100	Energy: 0.3020J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_303	Params: 616B	Quant: FP8	Efficacy: 83/100	Energy: 0.3030J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_304	Params: 618B	Quant: INT4	Efficacy: 84/100	Energy: 0.3040J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_305	Params: 620B	Quant: FP8	Efficacy: 85/100	Energy: 0.3050J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_306	Params: 622B	Quant: INT4	Efficacy: 86/100	Energy: 0.3060J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_307	Params: 624B	Quant: FP8	Efficacy: 87/100	Energy: 0.3070J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_308	Params: 626B	Quant: INT4	Efficacy: 88/100	Energy: 0.3080J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_309	Params: 628B	Quant: FP8	Efficacy: 89/100	Energy: 0.3090J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_310	Params: 630B	Quant: INT4	Efficacy: 90/100	Energy: 0.3100J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_311	Params: 632B	Quant: FP8	Efficacy: 91/100	Energy: 0.3110J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_312	Params: 634B	Quant: INT4	Efficacy: 92/100	Energy: 0.3120J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_313	Params: 636B	Quant: FP8	Efficacy: 93/100	Energy: 0.3130J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_314	Params: 638B	Quant: INT4	Efficacy: 94/100	Energy: 0.3140J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_315	Params: 640B	Quant: FP8	Efficacy: 95/100	Energy: 0.3150J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_316	Params: 642B	Quant: INT4	Efficacy: 96/100	Energy: 0.3160J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_317	Params: 644B	Quant: FP8	Efficacy: 97/100	Energy: 0.3170J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_318	Params: 646B	Quant: INT4	Efficacy: 98/100	Energy: 0.3180J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_319	Params: 648B	Quant: FP8	Efficacy: 99/100	Energy: 0.3190J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_320	Params: 650B	Quant: INT4	Efficacy: 60/100	Energy: 0.3200J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_321	Params: 652B	Quant: FP8	Efficacy: 61/100	Energy: 0.3210J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_322	Params: 654B	Quant: INT4	Efficacy: 62/100	Energy: 0.3220J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_323	Params: 656B	Quant: FP8	Efficacy: 63/100	Energy: 0.3230J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_324	Params: 658B	Quant: INT4	Efficacy: 64/100	Energy: 0.3240J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_325	Params: 660B	Quant: FP8	Efficacy: 65/100	Energy: 0.3250J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_326	Params: 662B	Quant: INT4	Efficacy: 66/100	Energy: 0.3260J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_327	Params: 664B	Quant: FP8	Efficacy: 67/100	Energy: 0.3270J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_328	Params: 666B	Quant: INT4	Efficacy: 68/100	Energy: 0.3280J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_329	Params: 668B	Quant: FP8	Efficacy: 69/100	Energy: 0.3290J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_330	Params: 670B	Quant: INT4	Efficacy: 70/100	Energy: 0.3300J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_331	Params: 672B	Quant: FP8	Efficacy: 71/100	Energy: 0.3310J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_332	Params: 674B	Quant: INT4	Efficacy: 72/100	Energy: 0.3320J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_333	Params: 676B	Quant: FP8	Efficacy: 73/100	Energy: 0.3330J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_334	Params: 678B	Quant: INT4	Efficacy: 74/100	Energy: 0.3340J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_335	Params: 680B	Quant: FP8	Efficacy: 75/100	Energy: 0.3350J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_336	Params: 682B	Quant: INT4	Efficacy: 76/100	Energy: 0.3360J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_337	Params: 684B	Quant: FP8	Efficacy: 77/100	Energy: 0.3370J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_338	Params: 686B	Quant: INT4	Efficacy: 78/100	Energy: 0.3380J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_339	Params: 688B	Quant: FP8	Efficacy: 79/100	Energy: 0.3390J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_340	Params: 690B	Quant: INT4	Efficacy: 80/100	Energy: 0.3400J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_341	Params: 692B	Quant: FP8	Efficacy: 81/100	Energy: 0.3410J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_342	Params: 694B	Quant: INT4	Efficacy: 82/100	Energy: 0.3420J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_343	Params: 696B	Quant: FP8	Efficacy: 83/100	Energy: 0.3430J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_344	Params: 698B	Quant: INT4	Efficacy: 84/100	Energy: 0.3440J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_345	Params: 700B	Quant: FP8	Efficacy: 85/100	Energy: 0.3450J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_346	Params: 702B	Quant: INT4	Efficacy: 86/100	Energy: 0.3460J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_347	Params: 704B	Quant: FP8	Efficacy: 87/100	Energy: 0.3470J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_348	Params: 706B	Quant: INT4	Efficacy: 88/100	Energy: 0.3480J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_349	Params: 708B	Quant: FP8	Efficacy: 89/100	Energy: 0.3490J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_350	Params: 710B	Quant: INT4	Efficacy: 90/100	Energy: 0.3500J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_351	Params: 712B	Quant: FP8	Efficacy: 91/100	Energy: 0.3510J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_352	Params: 714B	Quant: INT4	Efficacy: 92/100	Energy: 0.3520J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_353	Params: 716B	Quant: FP8	Efficacy: 93/100	Energy: 0.3530J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_354	Params: 718B	Quant: INT4	Efficacy: 94/100	Energy: 0.3540J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_355	Params: 720B	Quant: FP8	Efficacy: 95/100	Energy: 0.3550J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_356	Params: 722B	Quant: INT4	Efficacy: 96/100	Energy: 0.3560J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_357	Params: 724B	Quant: FP8	Efficacy: 97/100	Energy: 0.3570J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_358	Params: 726B	Quant: INT4	Efficacy: 98/100	Energy: 0.3580J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_359	Params: 728B	Quant: FP8	Efficacy: 99/100	Energy: 0.3590J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_360	Params: 730B	Quant: INT4	Efficacy: 60/100	Energy: 0.3600J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_361	Params: 732B	Quant: FP8	Efficacy: 61/100	Energy: 0.3610J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_362	Params: 734B	Quant: INT4	Efficacy: 62/100	Energy: 0.3620J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_363	Params: 736B	Quant: FP8	Efficacy: 63/100	Energy: 0.3630J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_364	Params: 738B	Quant: INT4	Efficacy: 64/100	Energy: 0.3640J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_365	Params: 740B	Quant: FP8	Efficacy: 65/100	Energy: 0.3650J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_366	Params: 742B	Quant: INT4	Efficacy: 66/100	Energy: 0.3660J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_367	Params: 744B	Quant: FP8	Efficacy: 67/100	Energy: 0.3670J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_368	Params: 746B	Quant: INT4	Efficacy: 68/100	Energy: 0.3680J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_369	Params: 748B	Quant: FP8	Efficacy: 69/100	Energy: 0.3690J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_370	Params: 750B	Quant: INT4	Efficacy: 70/100	Energy: 0.3700J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_371	Params: 752B	Quant: FP8	Efficacy: 71/100	Energy: 0.3710J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_372	Params: 754B	Quant: INT4	Efficacy: 72/100	Energy: 0.3720J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_373	Params: 756B	Quant: FP8	Efficacy: 73/100	Energy: 0.3730J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_374	Params: 758B	Quant: INT4	Efficacy: 74/100	Energy: 0.3740J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_375	Params: 760B	Quant: FP8	Efficacy: 75/100	Energy: 0.3750J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_376	Params: 762B	Quant: INT4	Efficacy: 76/100	Energy: 0.3760J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_377	Params: 764B	Quant: FP8	Efficacy: 77/100	Energy: 0.3770J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_378	Params: 766B	Quant: INT4	Efficacy: 78/100	Energy: 0.3780J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_379	Params: 768B	Quant: FP8	Efficacy: 79/100	Energy: 0.3790J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_380	Params: 770B	Quant: INT4	Efficacy: 80/100	Energy: 0.3800J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_381	Params: 772B	Quant: FP8	Efficacy: 81/100	Energy: 0.3810J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_382	Params: 774B	Quant: INT4	Efficacy: 82/100	Energy: 0.3820J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_383	Params: 776B	Quant: FP8	Efficacy: 83/100	Energy: 0.3830J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_384	Params: 778B	Quant: INT4	Efficacy: 84/100	Energy: 0.3840J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_385	Params: 780B	Quant: FP8	Efficacy: 85/100	Energy: 0.3850J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_386	Params: 782B	Quant: INT4	Efficacy: 86/100	Energy: 0.3860J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_387	Params: 784B	Quant: FP8	Efficacy: 87/100	Energy: 0.3870J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_388	Params: 786B	Quant: INT4	Efficacy: 88/100	Energy: 0.3880J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_389	Params: 788B	Quant: FP8	Efficacy: 89/100	Energy: 0.3890J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_390	Params: 790B	Quant: INT4	Efficacy: 90/100	Energy: 0.3900J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_391	Params: 792B	Quant: FP8	Efficacy: 91/100	Energy: 0.3910J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_392	Params: 794B	Quant: INT4	Efficacy: 92/100	Energy: 0.3920J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_393	Params: 796B	Quant: FP8	Efficacy: 93/100	Energy: 0.3930J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_394	Params: 798B	Quant: INT4	Efficacy: 94/100	Energy: 0.3940J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_395	Params: 800B	Quant: FP8	Efficacy: 95/100	Energy: 0.3950J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_396	Params: 802B	Quant: INT4	Efficacy: 96/100	Energy: 0.3960J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_397	Params: 804B	Quant: FP8	Efficacy: 97/100	Energy: 0.3970J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_398	Params: 806B	Quant: INT4	Efficacy: 98/100	Energy: 0.3980J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_399	Params: 808B	Quant: FP8	Efficacy: 99/100	Energy: 0.3990J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_400	Params: 810B	Quant: INT4	Efficacy: 60/100	Energy: 0.4000J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_401	Params: 812B	Quant: FP8	Efficacy: 61/100	Energy: 0.4010J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_402	Params: 814B	Quant: INT4	Efficacy: 62/100	Energy: 0.4020J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_403	Params: 816B	Quant: FP8	Efficacy: 63/100	Energy: 0.4030J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_404	Params: 818B	Quant: INT4	Efficacy: 64/100	Energy: 0.4040J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_405	Params: 820B	Quant: FP8	Efficacy: 65/100	Energy: 0.4050J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_406	Params: 822B	Quant: INT4	Efficacy: 66/100	Energy: 0.4060J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_407	Params: 824B	Quant: FP8	Efficacy: 67/100	Energy: 0.4070J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_408	Params: 826B	Quant: INT4	Efficacy: 68/100	Energy: 0.4080J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_409	Params: 828B	Quant: FP8	Efficacy: 69/100	Energy: 0.4090J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_410	Params: 830B	Quant: INT4	Efficacy: 70/100	Energy: 0.4100J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_411	Params: 832B	Quant: FP8	Efficacy: 71/100	Energy: 0.4110J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_412	Params: 834B	Quant: INT4	Efficacy: 72/100	Energy: 0.4120J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_413	Params: 836B	Quant: FP8	Efficacy: 73/100	Energy: 0.4130J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_414	Params: 838B	Quant: INT4	Efficacy: 74/100	Energy: 0.4140J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_415	Params: 840B	Quant: FP8	Efficacy: 75/100	Energy: 0.4150J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_416	Params: 842B	Quant: INT4	Efficacy: 76/100	Energy: 0.4160J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_417	Params: 844B	Quant: FP8	Efficacy: 77/100	Energy: 0.4170J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_418	Params: 846B	Quant: INT4	Efficacy: 78/100	Energy: 0.4180J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_419	Params: 848B	Quant: FP8	Efficacy: 79/100	Energy: 0.4190J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_420	Params: 850B	Quant: INT4	Efficacy: 80/100	Energy: 0.4200J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_421	Params: 852B	Quant: FP8	Efficacy: 81/100	Energy: 0.4210J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_422	Params: 854B	Quant: INT4	Efficacy: 82/100	Energy: 0.4220J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_423	Params: 856B	Quant: FP8	Efficacy: 83/100	Energy: 0.4230J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_424	Params: 858B	Quant: INT4	Efficacy: 84/100	Energy: 0.4240J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_425	Params: 860B	Quant: FP8	Efficacy: 85/100	Energy: 0.4250J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_426	Params: 862B	Quant: INT4	Efficacy: 86/100	Energy: 0.4260J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_427	Params: 864B	Quant: FP8	Efficacy: 87/100	Energy: 0.4270J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_428	Params: 866B	Quant: INT4	Efficacy: 88/100	Energy: 0.4280J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_429	Params: 868B	Quant: FP8	Efficacy: 89/100	Energy: 0.4290J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_430	Params: 870B	Quant: INT4	Efficacy: 90/100	Energy: 0.4300J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_431	Params: 872B	Quant: FP8	Efficacy: 91/100	Energy: 0.4310J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_432	Params: 874B	Quant: INT4	Efficacy: 92/100	Energy: 0.4320J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_433	Params: 876B	Quant: FP8	Efficacy: 93/100	Energy: 0.4330J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_434	Params: 878B	Quant: INT4	Efficacy: 94/100	Energy: 0.4340J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_435	Params: 880B	Quant: FP8	Efficacy: 95/100	Energy: 0.4350J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_436	Params: 882B	Quant: INT4	Efficacy: 96/100	Energy: 0.4360J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_437	Params: 884B	Quant: FP8	Efficacy: 97/100	Energy: 0.4370J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_438	Params: 886B	Quant: INT4	Efficacy: 98/100	Energy: 0.4380J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_439	Params: 888B	Quant: FP8	Efficacy: 99/100	Energy: 0.4390J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_440	Params: 890B	Quant: INT4	Efficacy: 60/100	Energy: 0.4400J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_441	Params: 892B	Quant: FP8	Efficacy: 61/100	Energy: 0.4410J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_442	Params: 894B	Quant: INT4	Efficacy: 62/100	Energy: 0.4420J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_443	Params: 896B	Quant: FP8	Efficacy: 63/100	Energy: 0.4430J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_444	Params: 898B	Quant: INT4	Efficacy: 64/100	Energy: 0.4440J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_445	Params: 900B	Quant: FP8	Efficacy: 65/100	Energy: 0.4450J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_446	Params: 902B	Quant: INT4	Efficacy: 66/100	Energy: 0.4460J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_447	Params: 904B	Quant: FP8	Efficacy: 67/100	Energy: 0.4470J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_448	Params: 906B	Quant: INT4	Efficacy: 68/100	Energy: 0.4480J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_449	Params: 908B	Quant: FP8	Efficacy: 69/100	Energy: 0.4490J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_450	Params: 910B	Quant: INT4	Efficacy: 70/100	Energy: 0.4500J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_451	Params: 912B	Quant: FP8	Efficacy: 71/100	Energy: 0.4510J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_452	Params: 914B	Quant: INT4	Efficacy: 72/100	Energy: 0.4520J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_453	Params: 916B	Quant: FP8	Efficacy: 73/100	Energy: 0.4530J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_454	Params: 918B	Quant: INT4	Efficacy: 74/100	Energy: 0.4540J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_455	Params: 920B	Quant: FP8	Efficacy: 75/100	Energy: 0.4550J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_456	Params: 922B	Quant: INT4	Efficacy: 76/100	Energy: 0.4560J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_457	Params: 924B	Quant: FP8	Efficacy: 77/100	Energy: 0.4570J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_458	Params: 926B	Quant: INT4	Efficacy: 78/100	Energy: 0.4580J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_459	Params: 928B	Quant: FP8	Efficacy: 79/100	Energy: 0.4590J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_460	Params: 930B	Quant: INT4	Efficacy: 80/100	Energy: 0.4600J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_461	Params: 932B	Quant: FP8	Efficacy: 81/100	Energy: 0.4610J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_462	Params: 934B	Quant: INT4	Efficacy: 82/100	Energy: 0.4620J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_463	Params: 936B	Quant: FP8	Efficacy: 83/100	Energy: 0.4630J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_464	Params: 938B	Quant: INT4	Efficacy: 84/100	Energy: 0.4640J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_465	Params: 940B	Quant: FP8	Efficacy: 85/100	Energy: 0.4650J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_466	Params: 942B	Quant: INT4	Efficacy: 86/100	Energy: 0.4660J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_467	Params: 944B	Quant: FP8	Efficacy: 87/100	Energy: 0.4670J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_468	Params: 946B	Quant: INT4	Efficacy: 88/100	Energy: 0.4680J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_469	Params: 948B	Quant: FP8	Efficacy: 89/100	Energy: 0.4690J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_470	Params: 950B	Quant: INT4	Efficacy: 90/100	Energy: 0.4700J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_471	Params: 952B	Quant: FP8	Efficacy: 91/100	Energy: 0.4710J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_472	Params: 954B	Quant: INT4	Efficacy: 92/100	Energy: 0.4720J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_473	Params: 956B	Quant: FP8	Efficacy: 93/100	Energy: 0.4730J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_474	Params: 958B	Quant: INT4	Efficacy: 94/100	Energy: 0.4740J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_475	Params: 960B	Quant: FP8	Efficacy: 95/100	Energy: 0.4750J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_476	Params: 962B	Quant: INT4	Efficacy: 96/100	Energy: 0.4760J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_477	Params: 964B	Quant: FP8	Efficacy: 97/100	Energy: 0.4770J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_478	Params: 966B	Quant: INT4	Efficacy: 98/100	Energy: 0.4780J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_479	Params: 968B	Quant: FP8	Efficacy: 99/100	Energy: 0.4790J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_480	Params: 970B	Quant: INT4	Efficacy: 60/100	Energy: 0.4800J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_481	Params: 972B	Quant: FP8	Efficacy: 61/100	Energy: 0.4810J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_482	Params: 974B	Quant: INT4	Efficacy: 62/100	Energy: 0.4820J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_483	Params: 976B	Quant: FP8	Efficacy: 63/100	Energy: 0.4830J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_484	Params: 978B	Quant: INT4	Efficacy: 64/100	Energy: 0.4840J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_485	Params: 980B	Quant: FP8	Efficacy: 65/100	Energy: 0.4850J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_486	Params: 982B	Quant: INT4	Efficacy: 66/100	Energy: 0.4860J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_487	Params: 984B	Quant: FP8	Efficacy: 67/100	Energy: 0.4870J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_488	Params: 986B	Quant: INT4	Efficacy: 68/100	Energy: 0.4880J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_489	Params: 988B	Quant: FP8	Efficacy: 69/100	Energy: 0.4890J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_490	Params: 990B	Quant: INT4	Efficacy: 70/100	Energy: 0.4900J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_491	Params: 992B	Quant: FP8	Efficacy: 71/100	Energy: 0.4910J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_492	Params: 994B	Quant: INT4	Efficacy: 72/100	Energy: 0.4920J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_493	Params: 996B	Quant: FP8	Efficacy: 73/100	Energy: 0.4930J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_494	Params: 998B	Quant: INT4	Efficacy: 74/100	Energy: 0.4940J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_495	Params: 1000B	Quant: FP8	Efficacy: 75/100	Energy: 0.4950J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_496	Params: 1002B	Quant: INT4	Efficacy: 76/100	Energy: 0.4960J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_497	Params: 1004B	Quant: FP8	Efficacy: 77/100	Energy: 0.4970J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_498	Params: 1006B	Quant: INT4	Efficacy: 78/100	Energy: 0.4980J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_499	Params: 1008B	Quant: FP8	Efficacy: 79/100	Energy: 0.4990J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_500	Params: 1010B	Quant: INT4	Efficacy: 80/100	Energy: 0.5000J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_501	Params: 1012B	Quant: FP8	Efficacy: 81/100	Energy: 0.5010J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_502	Params: 1014B	Quant: INT4	Efficacy: 82/100	Energy: 0.5020J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_503	Params: 1016B	Quant: FP8	Efficacy: 83/100	Energy: 0.5030J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_504	Params: 1018B	Quant: INT4	Efficacy: 84/100	Energy: 0.5040J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_505	Params: 1020B	Quant: FP8	Efficacy: 85/100	Energy: 0.5050J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_506	Params: 1022B	Quant: INT4	Efficacy: 86/100	Energy: 0.5060J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_507	Params: 1024B	Quant: FP8	Efficacy: 87/100	Energy: 0.5070J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_508	Params: 1026B	Quant: INT4	Efficacy: 88/100	Energy: 0.5080J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_509	Params: 1028B	Quant: FP8	Efficacy: 89/100	Energy: 0.5090J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_510	Params: 1030B	Quant: INT4	Efficacy: 90/100	Energy: 0.5100J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_511	Params: 1032B	Quant: FP8	Efficacy: 91/100	Energy: 0.5110J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_512	Params: 1034B	Quant: INT4	Efficacy: 92/100	Energy: 0.5120J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_513	Params: 1036B	Quant: FP8	Efficacy: 93/100	Energy: 0.5130J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_514	Params: 1038B	Quant: INT4	Efficacy: 94/100	Energy: 0.5140J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_515	Params: 1040B	Quant: FP8	Efficacy: 95/100	Energy: 0.5150J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_516	Params: 1042B	Quant: INT4	Efficacy: 96/100	Energy: 0.5160J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_517	Params: 1044B	Quant: FP8	Efficacy: 97/100	Energy: 0.5170J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_518	Params: 1046B	Quant: INT4	Efficacy: 98/100	Energy: 0.5180J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_519	Params: 1048B	Quant: FP8	Efficacy: 99/100	Energy: 0.5190J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_520	Params: 1050B	Quant: INT4	Efficacy: 60/100	Energy: 0.5200J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_521	Params: 1052B	Quant: FP8	Efficacy: 61/100	Energy: 0.5210J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_522	Params: 1054B	Quant: INT4	Efficacy: 62/100	Energy: 0.5220J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_523	Params: 1056B	Quant: FP8	Efficacy: 63/100	Energy: 0.5230J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_524	Params: 1058B	Quant: INT4	Efficacy: 64/100	Energy: 0.5240J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_525	Params: 1060B	Quant: FP8	Efficacy: 65/100	Energy: 0.5250J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_526	Params: 1062B	Quant: INT4	Efficacy: 66/100	Energy: 0.5260J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_527	Params: 1064B	Quant: FP8	Efficacy: 67/100	Energy: 0.5270J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_528	Params: 1066B	Quant: INT4	Efficacy: 68/100	Energy: 0.5280J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_529	Params: 1068B	Quant: FP8	Efficacy: 69/100	Energy: 0.5290J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_530	Params: 1070B	Quant: INT4	Efficacy: 70/100	Energy: 0.5300J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_531	Params: 1072B	Quant: FP8	Efficacy: 71/100	Energy: 0.5310J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_532	Params: 1074B	Quant: INT4	Efficacy: 72/100	Energy: 0.5320J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_533	Params: 1076B	Quant: FP8	Efficacy: 73/100	Energy: 0.5330J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_534	Params: 1078B	Quant: INT4	Efficacy: 74/100	Energy: 0.5340J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_535	Params: 1080B	Quant: FP8	Efficacy: 75/100	Energy: 0.5350J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_536	Params: 1082B	Quant: INT4	Efficacy: 76/100	Energy: 0.5360J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_537	Params: 1084B	Quant: FP8	Efficacy: 77/100	Energy: 0.5370J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_538	Params: 1086B	Quant: INT4	Efficacy: 78/100	Energy: 0.5380J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_539	Params: 1088B	Quant: FP8	Efficacy: 79/100	Energy: 0.5390J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_540	Params: 1090B	Quant: INT4	Efficacy: 80/100	Energy: 0.5400J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_541	Params: 1092B	Quant: FP8	Efficacy: 81/100	Energy: 0.5410J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_542	Params: 1094B	Quant: INT4	Efficacy: 82/100	Energy: 0.5420J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_543	Params: 1096B	Quant: FP8	Efficacy: 83/100	Energy: 0.5430J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_544	Params: 1098B	Quant: INT4	Efficacy: 84/100	Energy: 0.5440J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_545	Params: 1100B	Quant: FP8	Efficacy: 85/100	Energy: 0.5450J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_546	Params: 1102B	Quant: INT4	Efficacy: 86/100	Energy: 0.5460J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_547	Params: 1104B	Quant: FP8	Efficacy: 87/100	Energy: 0.5470J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_548	Params: 1106B	Quant: INT4	Efficacy: 88/100	Energy: 0.5480J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_549	Params: 1108B	Quant: FP8	Efficacy: 89/100	Energy: 0.5490J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_550	Params: 1110B	Quant: INT4	Efficacy: 90/100	Energy: 0.5500J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_551	Params: 1112B	Quant: FP8	Efficacy: 91/100	Energy: 0.5510J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_552	Params: 1114B	Quant: INT4	Efficacy: 92/100	Energy: 0.5520J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_553	Params: 1116B	Quant: FP8	Efficacy: 93/100	Energy: 0.5530J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_554	Params: 1118B	Quant: INT4	Efficacy: 94/100	Energy: 0.5540J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_555	Params: 1120B	Quant: FP8	Efficacy: 95/100	Energy: 0.5550J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_556	Params: 1122B	Quant: INT4	Efficacy: 96/100	Energy: 0.5560J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_557	Params: 1124B	Quant: FP8	Efficacy: 97/100	Energy: 0.5570J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_558	Params: 1126B	Quant: INT4	Efficacy: 98/100	Energy: 0.5580J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_559	Params: 1128B	Quant: FP8	Efficacy: 99/100	Energy: 0.5590J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_560	Params: 1130B	Quant: INT4	Efficacy: 60/100	Energy: 0.5600J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_561	Params: 1132B	Quant: FP8	Efficacy: 61/100	Energy: 0.5610J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_562	Params: 1134B	Quant: INT4	Efficacy: 62/100	Energy: 0.5620J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_563	Params: 1136B	Quant: FP8	Efficacy: 63/100	Energy: 0.5630J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_564	Params: 1138B	Quant: INT4	Efficacy: 64/100	Energy: 0.5640J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_565	Params: 1140B	Quant: FP8	Efficacy: 65/100	Energy: 0.5650J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_566	Params: 1142B	Quant: INT4	Efficacy: 66/100	Energy: 0.5660J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_567	Params: 1144B	Quant: FP8	Efficacy: 67/100	Energy: 0.5670J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_568	Params: 1146B	Quant: INT4	Efficacy: 68/100	Energy: 0.5680J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_569	Params: 1148B	Quant: FP8	Efficacy: 69/100	Energy: 0.5690J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_570	Params: 1150B	Quant: INT4	Efficacy: 70/100	Energy: 0.5700J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_571	Params: 1152B	Quant: FP8	Efficacy: 71/100	Energy: 0.5710J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_572	Params: 1154B	Quant: INT4	Efficacy: 72/100	Energy: 0.5720J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_573	Params: 1156B	Quant: FP8	Efficacy: 73/100	Energy: 0.5730J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_574	Params: 1158B	Quant: INT4	Efficacy: 74/100	Energy: 0.5740J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_575	Params: 1160B	Quant: FP8	Efficacy: 75/100	Energy: 0.5750J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_576	Params: 1162B	Quant: INT4	Efficacy: 76/100	Energy: 0.5760J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_577	Params: 1164B	Quant: FP8	Efficacy: 77/100	Energy: 0.5770J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_578	Params: 1166B	Quant: INT4	Efficacy: 78/100	Energy: 0.5780J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_579	Params: 1168B	Quant: FP8	Efficacy: 79/100	Energy: 0.5790J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_580	Params: 1170B	Quant: INT4	Efficacy: 80/100	Energy: 0.5800J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_581	Params: 1172B	Quant: FP8	Efficacy: 81/100	Energy: 0.5810J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_582	Params: 1174B	Quant: INT4	Efficacy: 82/100	Energy: 0.5820J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_583	Params: 1176B	Quant: FP8	Efficacy: 83/100	Energy: 0.5830J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_584	Params: 1178B	Quant: INT4	Efficacy: 84/100	Energy: 0.5840J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_585	Params: 1180B	Quant: FP8	Efficacy: 85/100	Energy: 0.5850J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_586	Params: 1182B	Quant: INT4	Efficacy: 86/100	Energy: 0.5860J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_587	Params: 1184B	Quant: FP8	Efficacy: 87/100	Energy: 0.5870J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_588	Params: 1186B	Quant: INT4	Efficacy: 88/100	Energy: 0.5880J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_589	Params: 1188B	Quant: FP8	Efficacy: 89/100	Energy: 0.5890J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_590	Params: 1190B	Quant: INT4	Efficacy: 90/100	Energy: 0.5900J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_591	Params: 1192B	Quant: FP8	Efficacy: 91/100	Energy: 0.5910J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_592	Params: 1194B	Quant: INT4	Efficacy: 92/100	Energy: 0.5920J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_593	Params: 1196B	Quant: FP8	Efficacy: 93/100	Energy: 0.5930J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_594	Params: 1198B	Quant: INT4	Efficacy: 94/100	Energy: 0.5940J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_595	Params: 1200B	Quant: FP8	Efficacy: 95/100	Energy: 0.5950J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_596	Params: 1202B	Quant: INT4	Efficacy: 96/100	Energy: 0.5960J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_597	Params: 1204B	Quant: FP8	Efficacy: 97/100	Energy: 0.5970J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_598	Params: 1206B	Quant: INT4	Efficacy: 98/100	Energy: 0.5980J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_599	Params: 1208B	Quant: FP8	Efficacy: 99/100	Energy: 0.5990J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_600	Params: 1210B	Quant: INT4	Efficacy: 60/100	Energy: 0.6000J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_601	Params: 1212B	Quant: FP8	Efficacy: 61/100	Energy: 0.6010J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_602	Params: 1214B	Quant: INT4	Efficacy: 62/100	Energy: 0.6020J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_603	Params: 1216B	Quant: FP8	Efficacy: 63/100	Energy: 0.6030J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_604	Params: 1218B	Quant: INT4	Efficacy: 64/100	Energy: 0.6040J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_605	Params: 1220B	Quant: FP8	Efficacy: 65/100	Energy: 0.6050J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_606	Params: 1222B	Quant: INT4	Efficacy: 66/100	Energy: 0.6060J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_607	Params: 1224B	Quant: FP8	Efficacy: 67/100	Energy: 0.6070J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_608	Params: 1226B	Quant: INT4	Efficacy: 68/100	Energy: 0.6080J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_609	Params: 1228B	Quant: FP8	Efficacy: 69/100	Energy: 0.6090J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_610	Params: 1230B	Quant: INT4	Efficacy: 70/100	Energy: 0.6100J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_611	Params: 1232B	Quant: FP8	Efficacy: 71/100	Energy: 0.6110J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_612	Params: 1234B	Quant: INT4	Efficacy: 72/100	Energy: 0.6120J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_613	Params: 1236B	Quant: FP8	Efficacy: 73/100	Energy: 0.6130J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_614	Params: 1238B	Quant: INT4	Efficacy: 74/100	Energy: 0.6140J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_615	Params: 1240B	Quant: FP8	Efficacy: 75/100	Energy: 0.6150J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_616	Params: 1242B	Quant: INT4	Efficacy: 76/100	Energy: 0.6160J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_617	Params: 1244B	Quant: FP8	Efficacy: 77/100	Energy: 0.6170J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_618	Params: 1246B	Quant: INT4	Efficacy: 78/100	Energy: 0.6180J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_619	Params: 1248B	Quant: FP8	Efficacy: 79/100	Energy: 0.6190J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_620	Params: 1250B	Quant: INT4	Efficacy: 80/100	Energy: 0.6200J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_621	Params: 1252B	Quant: FP8	Efficacy: 81/100	Energy: 0.6210J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_622	Params: 1254B	Quant: INT4	Efficacy: 82/100	Energy: 0.6220J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_623	Params: 1256B	Quant: FP8	Efficacy: 83/100	Energy: 0.6230J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_624	Params: 1258B	Quant: INT4	Efficacy: 84/100	Energy: 0.6240J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_625	Params: 1260B	Quant: FP8	Efficacy: 85/100	Energy: 0.6250J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_626	Params: 1262B	Quant: INT4	Efficacy: 86/100	Energy: 0.6260J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_627	Params: 1264B	Quant: FP8	Efficacy: 87/100	Energy: 0.6270J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_628	Params: 1266B	Quant: INT4	Efficacy: 88/100	Energy: 0.6280J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_629	Params: 1268B	Quant: FP8	Efficacy: 89/100	Energy: 0.6290J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_630	Params: 1270B	Quant: INT4	Efficacy: 90/100	Energy: 0.6300J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_631	Params: 1272B	Quant: FP8	Efficacy: 91/100	Energy: 0.6310J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_632	Params: 1274B	Quant: INT4	Efficacy: 92/100	Energy: 0.6320J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_633	Params: 1276B	Quant: FP8	Efficacy: 93/100	Energy: 0.6330J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_634	Params: 1278B	Quant: INT4	Efficacy: 94/100	Energy: 0.6340J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_635	Params: 1280B	Quant: FP8	Efficacy: 95/100	Energy: 0.6350J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_636	Params: 1282B	Quant: INT4	Efficacy: 96/100	Energy: 0.6360J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_637	Params: 1284B	Quant: FP8	Efficacy: 97/100	Energy: 0.6370J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_638	Params: 1286B	Quant: INT4	Efficacy: 98/100	Energy: 0.6380J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_639	Params: 1288B	Quant: FP8	Efficacy: 99/100	Energy: 0.6390J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_640	Params: 1290B	Quant: INT4	Efficacy: 60/100	Energy: 0.6400J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_641	Params: 1292B	Quant: FP8	Efficacy: 61/100	Energy: 0.6410J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_642	Params: 1294B	Quant: INT4	Efficacy: 62/100	Energy: 0.6420J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_643	Params: 1296B	Quant: FP8	Efficacy: 63/100	Energy: 0.6430J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_644	Params: 1298B	Quant: INT4	Efficacy: 64/100	Energy: 0.6440J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_645	Params: 1300B	Quant: FP8	Efficacy: 65/100	Energy: 0.6450J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_646	Params: 1302B	Quant: INT4	Efficacy: 66/100	Energy: 0.6460J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_647	Params: 1304B	Quant: FP8	Efficacy: 67/100	Energy: 0.6470J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_648	Params: 1306B	Quant: INT4	Efficacy: 68/100	Energy: 0.6480J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_649	Params: 1308B	Quant: FP8	Efficacy: 69/100	Energy: 0.6490J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_650	Params: 1310B	Quant: INT4	Efficacy: 70/100	Energy: 0.6500J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_651	Params: 1312B	Quant: FP8	Efficacy: 71/100	Energy: 0.6510J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_652	Params: 1314B	Quant: INT4	Efficacy: 72/100	Energy: 0.6520J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_653	Params: 1316B	Quant: FP8	Efficacy: 73/100	Energy: 0.6530J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_654	Params: 1318B	Quant: INT4	Efficacy: 74/100	Energy: 0.6540J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_655	Params: 1320B	Quant: FP8	Efficacy: 75/100	Energy: 0.6550J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_656	Params: 1322B	Quant: INT4	Efficacy: 76/100	Energy: 0.6560J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_657	Params: 1324B	Quant: FP8	Efficacy: 77/100	Energy: 0.6570J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_658	Params: 1326B	Quant: INT4	Efficacy: 78/100	Energy: 0.6580J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_659	Params: 1328B	Quant: FP8	Efficacy: 79/100	Energy: 0.6590J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_660	Params: 1330B	Quant: INT4	Efficacy: 80/100	Energy: 0.6600J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_661	Params: 1332B	Quant: FP8	Efficacy: 81/100	Energy: 0.6610J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_662	Params: 1334B	Quant: INT4	Efficacy: 82/100	Energy: 0.6620J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_663	Params: 1336B	Quant: FP8	Efficacy: 83/100	Energy: 0.6630J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_664	Params: 1338B	Quant: INT4	Efficacy: 84/100	Energy: 0.6640J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_665	Params: 1340B	Quant: FP8	Efficacy: 85/100	Energy: 0.6650J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_666	Params: 1342B	Quant: INT4	Efficacy: 86/100	Energy: 0.6660J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_667	Params: 1344B	Quant: FP8	Efficacy: 87/100	Energy: 0.6670J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_668	Params: 1346B	Quant: INT4	Efficacy: 88/100	Energy: 0.6680J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_669	Params: 1348B	Quant: FP8	Efficacy: 89/100	Energy: 0.6690J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_670	Params: 1350B	Quant: INT4	Efficacy: 90/100	Energy: 0.6700J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_671	Params: 1352B	Quant: FP8	Efficacy: 91/100	Energy: 0.6710J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_672	Params: 1354B	Quant: INT4	Efficacy: 92/100	Energy: 0.6720J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_673	Params: 1356B	Quant: FP8	Efficacy: 93/100	Energy: 0.6730J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_674	Params: 1358B	Quant: INT4	Efficacy: 94/100	Energy: 0.6740J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_675	Params: 1360B	Quant: FP8	Efficacy: 95/100	Energy: 0.6750J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_676	Params: 1362B	Quant: INT4	Efficacy: 96/100	Energy: 0.6760J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_677	Params: 1364B	Quant: FP8	Efficacy: 97/100	Energy: 0.6770J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_678	Params: 1366B	Quant: INT4	Efficacy: 98/100	Energy: 0.6780J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_679	Params: 1368B	Quant: FP8	Efficacy: 99/100	Energy: 0.6790J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_680	Params: 1370B	Quant: INT4	Efficacy: 60/100	Energy: 0.6800J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_681	Params: 1372B	Quant: FP8	Efficacy: 61/100	Energy: 0.6810J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_682	Params: 1374B	Quant: INT4	Efficacy: 62/100	Energy: 0.6820J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_683	Params: 1376B	Quant: FP8	Efficacy: 63/100	Energy: 0.6830J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_684	Params: 1378B	Quant: INT4	Efficacy: 64/100	Energy: 0.6840J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_685	Params: 1380B	Quant: FP8	Efficacy: 65/100	Energy: 0.6850J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_686	Params: 1382B	Quant: INT4	Efficacy: 66/100	Energy: 0.6860J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_687	Params: 1384B	Quant: FP8	Efficacy: 67/100	Energy: 0.6870J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_688	Params: 1386B	Quant: INT4	Efficacy: 68/100	Energy: 0.6880J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_689	Params: 1388B	Quant: FP8	Efficacy: 69/100	Energy: 0.6890J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_690	Params: 1390B	Quant: INT4	Efficacy: 70/100	Energy: 0.6900J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_691	Params: 1392B	Quant: FP8	Efficacy: 71/100	Energy: 0.6910J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_692	Params: 1394B	Quant: INT4	Efficacy: 72/100	Energy: 0.6920J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_693	Params: 1396B	Quant: FP8	Efficacy: 73/100	Energy: 0.6930J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_694	Params: 1398B	Quant: INT4	Efficacy: 74/100	Energy: 0.6940J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_695	Params: 1400B	Quant: FP8	Efficacy: 75/100	Energy: 0.6950J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_696	Params: 1402B	Quant: INT4	Efficacy: 76/100	Energy: 0.6960J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_697	Params: 1404B	Quant: FP8	Efficacy: 77/100	Energy: 0.6970J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_698	Params: 1406B	Quant: INT4	Efficacy: 78/100	Energy: 0.6980J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_699	Params: 1408B	Quant: FP8	Efficacy: 79/100	Energy: 0.6990J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_700	Params: 1410B	Quant: INT4	Efficacy: 80/100	Energy: 0.7000J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_701	Params: 1412B	Quant: FP8	Efficacy: 81/100	Energy: 0.7010J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_702	Params: 1414B	Quant: INT4	Efficacy: 82/100	Energy: 0.7020J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_703	Params: 1416B	Quant: FP8	Efficacy: 83/100	Energy: 0.7030J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_704	Params: 1418B	Quant: INT4	Efficacy: 84/100	Energy: 0.7040J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_705	Params: 1420B	Quant: FP8	Efficacy: 85/100	Energy: 0.7050J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_706	Params: 1422B	Quant: INT4	Efficacy: 86/100	Energy: 0.7060J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_707	Params: 1424B	Quant: FP8	Efficacy: 87/100	Energy: 0.7070J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_708	Params: 1426B	Quant: INT4	Efficacy: 88/100	Energy: 0.7080J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_709	Params: 1428B	Quant: FP8	Efficacy: 89/100	Energy: 0.7090J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_710	Params: 1430B	Quant: INT4	Efficacy: 90/100	Energy: 0.7100J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_711	Params: 1432B	Quant: FP8	Efficacy: 91/100	Energy: 0.7110J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_712	Params: 1434B	Quant: INT4	Efficacy: 92/100	Energy: 0.7120J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_713	Params: 1436B	Quant: FP8	Efficacy: 93/100	Energy: 0.7130J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_714	Params: 1438B	Quant: INT4	Efficacy: 94/100	Energy: 0.7140J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_715	Params: 1440B	Quant: FP8	Efficacy: 95/100	Energy: 0.7150J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_716	Params: 1442B	Quant: INT4	Efficacy: 96/100	Energy: 0.7160J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_717	Params: 1444B	Quant: FP8	Efficacy: 97/100	Energy: 0.7170J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_718	Params: 1446B	Quant: INT4	Efficacy: 98/100	Energy: 0.7180J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_719	Params: 1448B	Quant: FP8	Efficacy: 99/100	Energy: 0.7190J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_720	Params: 1450B	Quant: INT4	Efficacy: 60/100	Energy: 0.7200J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_721	Params: 1452B	Quant: FP8	Efficacy: 61/100	Energy: 0.7210J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_722	Params: 1454B	Quant: INT4	Efficacy: 62/100	Energy: 0.7220J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_723	Params: 1456B	Quant: FP8	Efficacy: 63/100	Energy: 0.7230J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_724	Params: 1458B	Quant: INT4	Efficacy: 64/100	Energy: 0.7240J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_725	Params: 1460B	Quant: FP8	Efficacy: 65/100	Energy: 0.7250J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_726	Params: 1462B	Quant: INT4	Efficacy: 66/100	Energy: 0.7260J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_727	Params: 1464B	Quant: FP8	Efficacy: 67/100	Energy: 0.7270J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_728	Params: 1466B	Quant: INT4	Efficacy: 68/100	Energy: 0.7280J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_729	Params: 1468B	Quant: FP8	Efficacy: 69/100	Energy: 0.7290J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_730	Params: 1470B	Quant: INT4	Efficacy: 70/100	Energy: 0.7300J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_731	Params: 1472B	Quant: FP8	Efficacy: 71/100	Energy: 0.7310J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_732	Params: 1474B	Quant: INT4	Efficacy: 72/100	Energy: 0.7320J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_733	Params: 1476B	Quant: FP8	Efficacy: 73/100	Energy: 0.7330J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_734	Params: 1478B	Quant: INT4	Efficacy: 74/100	Energy: 0.7340J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_735	Params: 1480B	Quant: FP8	Efficacy: 75/100	Energy: 0.7350J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_736	Params: 1482B	Quant: INT4	Efficacy: 76/100	Energy: 0.7360J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_737	Params: 1484B	Quant: FP8	Efficacy: 77/100	Energy: 0.7370J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_738	Params: 1486B	Quant: INT4	Efficacy: 78/100	Energy: 0.7380J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_739	Params: 1488B	Quant: FP8	Efficacy: 79/100	Energy: 0.7390J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_740	Params: 1490B	Quant: INT4	Efficacy: 80/100	Energy: 0.7400J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_741	Params: 1492B	Quant: FP8	Efficacy: 81/100	Energy: 0.7410J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_742	Params: 1494B	Quant: INT4	Efficacy: 82/100	Energy: 0.7420J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_743	Params: 1496B	Quant: FP8	Efficacy: 83/100	Energy: 0.7430J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_744	Params: 1498B	Quant: INT4	Efficacy: 84/100	Energy: 0.7440J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_745	Params: 1500B	Quant: FP8	Efficacy: 85/100	Energy: 0.7450J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_746	Params: 1502B	Quant: INT4	Efficacy: 86/100	Energy: 0.7460J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_747	Params: 1504B	Quant: FP8	Efficacy: 87/100	Energy: 0.7470J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_748	Params: 1506B	Quant: INT4	Efficacy: 88/100	Energy: 0.7480J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_749	Params: 1508B	Quant: FP8	Efficacy: 89/100	Energy: 0.7490J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_750	Params: 1510B	Quant: INT4	Efficacy: 90/100	Energy: 0.7500J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_751	Params: 1512B	Quant: FP8	Efficacy: 91/100	Energy: 0.7510J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_752	Params: 1514B	Quant: INT4	Efficacy: 92/100	Energy: 0.7520J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_753	Params: 1516B	Quant: FP8	Efficacy: 93/100	Energy: 0.7530J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_754	Params: 1518B	Quant: INT4	Efficacy: 94/100	Energy: 0.7540J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_755	Params: 1520B	Quant: FP8	Efficacy: 95/100	Energy: 0.7550J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_756	Params: 1522B	Quant: INT4	Efficacy: 96/100	Energy: 0.7560J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_757	Params: 1524B	Quant: FP8	Efficacy: 97/100	Energy: 0.7570J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_758	Params: 1526B	Quant: INT4	Efficacy: 98/100	Energy: 0.7580J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_759	Params: 1528B	Quant: FP8	Efficacy: 99/100	Energy: 0.7590J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_760	Params: 1530B	Quant: INT4	Efficacy: 60/100	Energy: 0.7600J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_761	Params: 1532B	Quant: FP8	Efficacy: 61/100	Energy: 0.7610J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_762	Params: 1534B	Quant: INT4	Efficacy: 62/100	Energy: 0.7620J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_763	Params: 1536B	Quant: FP8	Efficacy: 63/100	Energy: 0.7630J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_764	Params: 1538B	Quant: INT4	Efficacy: 64/100	Energy: 0.7640J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_765	Params: 1540B	Quant: FP8	Efficacy: 65/100	Energy: 0.7650J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_766	Params: 1542B	Quant: INT4	Efficacy: 66/100	Energy: 0.7660J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_767	Params: 1544B	Quant: FP8	Efficacy: 67/100	Energy: 0.7670J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_768	Params: 1546B	Quant: INT4	Efficacy: 68/100	Energy: 0.7680J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_769	Params: 1548B	Quant: FP8	Efficacy: 69/100	Energy: 0.7690J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_770	Params: 1550B	Quant: INT4	Efficacy: 70/100	Energy: 0.7700J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_771	Params: 1552B	Quant: FP8	Efficacy: 71/100	Energy: 0.7710J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_772	Params: 1554B	Quant: INT4	Efficacy: 72/100	Energy: 0.7720J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_773	Params: 1556B	Quant: FP8	Efficacy: 73/100	Energy: 0.7730J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_774	Params: 1558B	Quant: INT4	Efficacy: 74/100	Energy: 0.7740J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_775	Params: 1560B	Quant: FP8	Efficacy: 75/100	Energy: 0.7750J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_776	Params: 1562B	Quant: INT4	Efficacy: 76/100	Energy: 0.7760J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_777	Params: 1564B	Quant: FP8	Efficacy: 77/100	Energy: 0.7770J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_778	Params: 1566B	Quant: INT4	Efficacy: 78/100	Energy: 0.7780J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_779	Params: 1568B	Quant: FP8	Efficacy: 79/100	Energy: 0.7790J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_780	Params: 1570B	Quant: INT4	Efficacy: 80/100	Energy: 0.7800J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_781	Params: 1572B	Quant: FP8	Efficacy: 81/100	Energy: 0.7810J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_782	Params: 1574B	Quant: INT4	Efficacy: 82/100	Energy: 0.7820J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_783	Params: 1576B	Quant: FP8	Efficacy: 83/100	Energy: 0.7830J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_784	Params: 1578B	Quant: INT4	Efficacy: 84/100	Energy: 0.7840J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_785	Params: 1580B	Quant: FP8	Efficacy: 85/100	Energy: 0.7850J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_786	Params: 1582B	Quant: INT4	Efficacy: 86/100	Energy: 0.7860J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_787	Params: 1584B	Quant: FP8	Efficacy: 87/100	Energy: 0.7870J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_788	Params: 1586B	Quant: INT4	Efficacy: 88/100	Energy: 0.7880J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_789	Params: 1588B	Quant: FP8	Efficacy: 89/100	Energy: 0.7890J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_790	Params: 1590B	Quant: INT4	Efficacy: 90/100	Energy: 0.7900J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_791	Params: 1592B	Quant: FP8	Efficacy: 91/100	Energy: 0.7910J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_792	Params: 1594B	Quant: INT4	Efficacy: 92/100	Energy: 0.7920J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_793	Params: 1596B	Quant: FP8	Efficacy: 93/100	Energy: 0.7930J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_794	Params: 1598B	Quant: INT4	Efficacy: 94/100	Energy: 0.7940J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_795	Params: 1600B	Quant: FP8	Efficacy: 95/100	Energy: 0.7950J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_796	Params: 1602B	Quant: INT4	Efficacy: 96/100	Energy: 0.7960J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_797	Params: 1604B	Quant: FP8	Efficacy: 97/100	Energy: 0.7970J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_798	Params: 1606B	Quant: INT4	Efficacy: 98/100	Energy: 0.7980J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_799	Params: 1608B	Quant: FP8	Efficacy: 99/100	Energy: 0.7990J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_800	Params: 1610B	Quant: INT4	Efficacy: 60/100	Energy: 0.8000J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_801	Params: 1612B	Quant: FP8	Efficacy: 61/100	Energy: 0.8010J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_802	Params: 1614B	Quant: INT4	Efficacy: 62/100	Energy: 0.8020J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_803	Params: 1616B	Quant: FP8	Efficacy: 63/100	Energy: 0.8030J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_804	Params: 1618B	Quant: INT4	Efficacy: 64/100	Energy: 0.8040J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_805	Params: 1620B	Quant: FP8	Efficacy: 65/100	Energy: 0.8050J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_806	Params: 1622B	Quant: INT4	Efficacy: 66/100	Energy: 0.8060J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_807	Params: 1624B	Quant: FP8	Efficacy: 67/100	Energy: 0.8070J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_808	Params: 1626B	Quant: INT4	Efficacy: 68/100	Energy: 0.8080J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_809	Params: 1628B	Quant: FP8	Efficacy: 69/100	Energy: 0.8090J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_810	Params: 1630B	Quant: INT4	Efficacy: 70/100	Energy: 0.8100J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_811	Params: 1632B	Quant: FP8	Efficacy: 71/100	Energy: 0.8110J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_812	Params: 1634B	Quant: INT4	Efficacy: 72/100	Energy: 0.8120J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_813	Params: 1636B	Quant: FP8	Efficacy: 73/100	Energy: 0.8130J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_814	Params: 1638B	Quant: INT4	Efficacy: 74/100	Energy: 0.8140J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_815	Params: 1640B	Quant: FP8	Efficacy: 75/100	Energy: 0.8150J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_816	Params: 1642B	Quant: INT4	Efficacy: 76/100	Energy: 0.8160J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_817	Params: 1644B	Quant: FP8	Efficacy: 77/100	Energy: 0.8170J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_818	Params: 1646B	Quant: INT4	Efficacy: 78/100	Energy: 0.8180J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_819	Params: 1648B	Quant: FP8	Efficacy: 79/100	Energy: 0.8190J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_820	Params: 1650B	Quant: INT4	Efficacy: 80/100	Energy: 0.8200J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_821	Params: 1652B	Quant: FP8	Efficacy: 81/100	Energy: 0.8210J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_822	Params: 1654B	Quant: INT4	Efficacy: 82/100	Energy: 0.8220J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_823	Params: 1656B	Quant: FP8	Efficacy: 83/100	Energy: 0.8230J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_824	Params: 1658B	Quant: INT4	Efficacy: 84/100	Energy: 0.8240J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_825	Params: 1660B	Quant: FP8	Efficacy: 85/100	Energy: 0.8250J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_826	Params: 1662B	Quant: INT4	Efficacy: 86/100	Energy: 0.8260J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_827	Params: 1664B	Quant: FP8	Efficacy: 87/100	Energy: 0.8270J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_828	Params: 1666B	Quant: INT4	Efficacy: 88/100	Energy: 0.8280J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_829	Params: 1668B	Quant: FP8	Efficacy: 89/100	Energy: 0.8290J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_830	Params: 1670B	Quant: INT4	Efficacy: 90/100	Energy: 0.8300J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_831	Params: 1672B	Quant: FP8	Efficacy: 91/100	Energy: 0.8310J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_832	Params: 1674B	Quant: INT4	Efficacy: 92/100	Energy: 0.8320J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_833	Params: 1676B	Quant: FP8	Efficacy: 93/100	Energy: 0.8330J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_834	Params: 1678B	Quant: INT4	Efficacy: 94/100	Energy: 0.8340J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_835	Params: 1680B	Quant: FP8	Efficacy: 95/100	Energy: 0.8350J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_836	Params: 1682B	Quant: INT4	Efficacy: 96/100	Energy: 0.8360J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_837	Params: 1684B	Quant: FP8	Efficacy: 97/100	Energy: 0.8370J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_838	Params: 1686B	Quant: INT4	Efficacy: 98/100	Energy: 0.8380J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_839	Params: 1688B	Quant: FP8	Efficacy: 99/100	Energy: 0.8390J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_840	Params: 1690B	Quant: INT4	Efficacy: 60/100	Energy: 0.8400J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_841	Params: 1692B	Quant: FP8	Efficacy: 61/100	Energy: 0.8410J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_842	Params: 1694B	Quant: INT4	Efficacy: 62/100	Energy: 0.8420J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_843	Params: 1696B	Quant: FP8	Efficacy: 63/100	Energy: 0.8430J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_844	Params: 1698B	Quant: INT4	Efficacy: 64/100	Energy: 0.8440J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_845	Params: 1700B	Quant: FP8	Efficacy: 65/100	Energy: 0.8450J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_846	Params: 1702B	Quant: INT4	Efficacy: 66/100	Energy: 0.8460J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_847	Params: 1704B	Quant: FP8	Efficacy: 67/100	Energy: 0.8470J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_848	Params: 1706B	Quant: INT4	Efficacy: 68/100	Energy: 0.8480J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_849	Params: 1708B	Quant: FP8	Efficacy: 69/100	Energy: 0.8490J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_850	Params: 1710B	Quant: INT4	Efficacy: 70/100	Energy: 0.8500J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_851	Params: 1712B	Quant: FP8	Efficacy: 71/100	Energy: 0.8510J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_852	Params: 1714B	Quant: INT4	Efficacy: 72/100	Energy: 0.8520J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_853	Params: 1716B	Quant: FP8	Efficacy: 73/100	Energy: 0.8530J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_854	Params: 1718B	Quant: INT4	Efficacy: 74/100	Energy: 0.8540J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_855	Params: 1720B	Quant: FP8	Efficacy: 75/100	Energy: 0.8550J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_856	Params: 1722B	Quant: INT4	Efficacy: 76/100	Energy: 0.8560J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_857	Params: 1724B	Quant: FP8	Efficacy: 77/100	Energy: 0.8570J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_858	Params: 1726B	Quant: INT4	Efficacy: 78/100	Energy: 0.8580J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_859	Params: 1728B	Quant: FP8	Efficacy: 79/100	Energy: 0.8590J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_860	Params: 1730B	Quant: INT4	Efficacy: 80/100	Energy: 0.8600J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_861	Params: 1732B	Quant: FP8	Efficacy: 81/100	Energy: 0.8610J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_862	Params: 1734B	Quant: INT4	Efficacy: 82/100	Energy: 0.8620J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_863	Params: 1736B	Quant: FP8	Efficacy: 83/100	Energy: 0.8630J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_864	Params: 1738B	Quant: INT4	Efficacy: 84/100	Energy: 0.8640J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_865	Params: 1740B	Quant: FP8	Efficacy: 85/100	Energy: 0.8650J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_866	Params: 1742B	Quant: INT4	Efficacy: 86/100	Energy: 0.8660J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_867	Params: 1744B	Quant: FP8	Efficacy: 87/100	Energy: 0.8670J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_868	Params: 1746B	Quant: INT4	Efficacy: 88/100	Energy: 0.8680J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_869	Params: 1748B	Quant: FP8	Efficacy: 89/100	Energy: 0.8690J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_870	Params: 1750B	Quant: INT4	Efficacy: 90/100	Energy: 0.8700J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_871	Params: 1752B	Quant: FP8	Efficacy: 91/100	Energy: 0.8710J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_872	Params: 1754B	Quant: INT4	Efficacy: 92/100	Energy: 0.8720J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_873	Params: 1756B	Quant: FP8	Efficacy: 93/100	Energy: 0.8730J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_874	Params: 1758B	Quant: INT4	Efficacy: 94/100	Energy: 0.8740J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_875	Params: 1760B	Quant: FP8	Efficacy: 95/100	Energy: 0.8750J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_876	Params: 1762B	Quant: INT4	Efficacy: 96/100	Energy: 0.8760J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_877	Params: 1764B	Quant: FP8	Efficacy: 97/100	Energy: 0.8770J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_878	Params: 1766B	Quant: INT4	Efficacy: 98/100	Energy: 0.8780J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_879	Params: 1768B	Quant: FP8	Efficacy: 99/100	Energy: 0.8790J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_880	Params: 1770B	Quant: INT4	Efficacy: 60/100	Energy: 0.8800J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_881	Params: 1772B	Quant: FP8	Efficacy: 61/100	Energy: 0.8810J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_882	Params: 1774B	Quant: INT4	Efficacy: 62/100	Energy: 0.8820J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_883	Params: 1776B	Quant: FP8	Efficacy: 63/100	Energy: 0.8830J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_884	Params: 1778B	Quant: INT4	Efficacy: 64/100	Energy: 0.8840J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_885	Params: 1780B	Quant: FP8	Efficacy: 65/100	Energy: 0.8850J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_886	Params: 1782B	Quant: INT4	Efficacy: 66/100	Energy: 0.8860J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_887	Params: 1784B	Quant: FP8	Efficacy: 67/100	Energy: 0.8870J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_888	Params: 1786B	Quant: INT4	Efficacy: 68/100	Energy: 0.8880J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_889	Params: 1788B	Quant: FP8	Efficacy: 69/100	Energy: 0.8890J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_890	Params: 1790B	Quant: INT4	Efficacy: 70/100	Energy: 0.8900J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_891	Params: 1792B	Quant: FP8	Efficacy: 71/100	Energy: 0.8910J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_892	Params: 1794B	Quant: INT4	Efficacy: 72/100	Energy: 0.8920J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_893	Params: 1796B	Quant: FP8	Efficacy: 73/100	Energy: 0.8930J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_894	Params: 1798B	Quant: INT4	Efficacy: 74/100	Energy: 0.8940J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_895	Params: 1800B	Quant: FP8	Efficacy: 75/100	Energy: 0.8950J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_896	Params: 1802B	Quant: INT4	Efficacy: 76/100	Energy: 0.8960J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_897	Params: 1804B	Quant: FP8	Efficacy: 77/100	Energy: 0.8970J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_898	Params: 1806B	Quant: INT4	Efficacy: 78/100	Energy: 0.8980J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_899	Params: 1808B	Quant: FP8	Efficacy: 79/100	Energy: 0.8990J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_900	Params: 1810B	Quant: INT4	Efficacy: 80/100	Energy: 0.9000J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_901	Params: 1812B	Quant: FP8	Efficacy: 81/100	Energy: 0.9010J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_902	Params: 1814B	Quant: INT4	Efficacy: 82/100	Energy: 0.9020J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_903	Params: 1816B	Quant: FP8	Efficacy: 83/100	Energy: 0.9030J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_904	Params: 1818B	Quant: INT4	Efficacy: 84/100	Energy: 0.9040J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_905	Params: 1820B	Quant: FP8	Efficacy: 85/100	Energy: 0.9050J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_906	Params: 1822B	Quant: INT4	Efficacy: 86/100	Energy: 0.9060J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_907	Params: 1824B	Quant: FP8	Efficacy: 87/100	Energy: 0.9070J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_908	Params: 1826B	Quant: INT4	Efficacy: 88/100	Energy: 0.9080J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_909	Params: 1828B	Quant: FP8	Efficacy: 89/100	Energy: 0.9090J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_910	Params: 1830B	Quant: INT4	Efficacy: 90/100	Energy: 0.9100J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_911	Params: 1832B	Quant: FP8	Efficacy: 91/100	Energy: 0.9110J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_912	Params: 1834B	Quant: INT4	Efficacy: 92/100	Energy: 0.9120J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_913	Params: 1836B	Quant: FP8	Efficacy: 93/100	Energy: 0.9130J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_914	Params: 1838B	Quant: INT4	Efficacy: 94/100	Energy: 0.9140J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_915	Params: 1840B	Quant: FP8	Efficacy: 95/100	Energy: 0.9150J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_916	Params: 1842B	Quant: INT4	Efficacy: 96/100	Energy: 0.9160J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_917	Params: 1844B	Quant: FP8	Efficacy: 97/100	Energy: 0.9170J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_918	Params: 1846B	Quant: INT4	Efficacy: 98/100	Energy: 0.9180J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_919	Params: 1848B	Quant: FP8	Efficacy: 99/100	Energy: 0.9190J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_920	Params: 1850B	Quant: INT4	Efficacy: 60/100	Energy: 0.9200J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_921	Params: 1852B	Quant: FP8	Efficacy: 61/100	Energy: 0.9210J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_922	Params: 1854B	Quant: INT4	Efficacy: 62/100	Energy: 0.9220J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_923	Params: 1856B	Quant: FP8	Efficacy: 63/100	Energy: 0.9230J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_924	Params: 1858B	Quant: INT4	Efficacy: 64/100	Energy: 0.9240J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_925	Params: 1860B	Quant: FP8	Efficacy: 65/100	Energy: 0.9250J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_926	Params: 1862B	Quant: INT4	Efficacy: 66/100	Energy: 0.9260J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_927	Params: 1864B	Quant: FP8	Efficacy: 67/100	Energy: 0.9270J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_928	Params: 1866B	Quant: INT4	Efficacy: 68/100	Energy: 0.9280J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_929	Params: 1868B	Quant: FP8	Efficacy: 69/100	Energy: 0.9290J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_930	Params: 1870B	Quant: INT4	Efficacy: 70/100	Energy: 0.9300J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_931	Params: 1872B	Quant: FP8	Efficacy: 71/100	Energy: 0.9310J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_932	Params: 1874B	Quant: INT4	Efficacy: 72/100	Energy: 0.9320J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_933	Params: 1876B	Quant: FP8	Efficacy: 73/100	Energy: 0.9330J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_934	Params: 1878B	Quant: INT4	Efficacy: 74/100	Energy: 0.9340J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_935	Params: 1880B	Quant: FP8	Efficacy: 75/100	Energy: 0.9350J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_936	Params: 1882B	Quant: INT4	Efficacy: 76/100	Energy: 0.9360J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_937	Params: 1884B	Quant: FP8	Efficacy: 77/100	Energy: 0.9370J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_938	Params: 1886B	Quant: INT4	Efficacy: 78/100	Energy: 0.9380J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_939	Params: 1888B	Quant: FP8	Efficacy: 79/100	Energy: 0.9390J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_940	Params: 1890B	Quant: INT4	Efficacy: 80/100	Energy: 0.9400J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_941	Params: 1892B	Quant: FP8	Efficacy: 81/100	Energy: 0.9410J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_942	Params: 1894B	Quant: INT4	Efficacy: 82/100	Energy: 0.9420J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_943	Params: 1896B	Quant: FP8	Efficacy: 83/100	Energy: 0.9430J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_944	Params: 1898B	Quant: INT4	Efficacy: 84/100	Energy: 0.9440J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_945	Params: 1900B	Quant: FP8	Efficacy: 85/100	Energy: 0.9450J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_946	Params: 1902B	Quant: INT4	Efficacy: 86/100	Energy: 0.9460J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_947	Params: 1904B	Quant: FP8	Efficacy: 87/100	Energy: 0.9470J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_948	Params: 1906B	Quant: INT4	Efficacy: 88/100	Energy: 0.9480J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_949	Params: 1908B	Quant: FP8	Efficacy: 89/100	Energy: 0.9490J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_950	Params: 1910B	Quant: INT4	Efficacy: 90/100	Energy: 0.9500J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_951	Params: 1912B	Quant: FP8	Efficacy: 91/100	Energy: 0.9510J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_952	Params: 1914B	Quant: INT4	Efficacy: 92/100	Energy: 0.9520J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_953	Params: 1916B	Quant: FP8	Efficacy: 93/100	Energy: 0.9530J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_954	Params: 1918B	Quant: INT4	Efficacy: 94/100	Energy: 0.9540J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_955	Params: 1920B	Quant: FP8	Efficacy: 95/100	Energy: 0.9550J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_956	Params: 1922B	Quant: INT4	Efficacy: 96/100	Energy: 0.9560J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_957	Params: 1924B	Quant: FP8	Efficacy: 97/100	Energy: 0.9570J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_958	Params: 1926B	Quant: INT4	Efficacy: 98/100	Energy: 0.9580J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_959	Params: 1928B	Quant: FP8	Efficacy: 99/100	Energy: 0.9590J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_960	Params: 1930B	Quant: INT4	Efficacy: 60/100	Energy: 0.9600J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_961	Params: 1932B	Quant: FP8	Efficacy: 61/100	Energy: 0.9610J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_962	Params: 1934B	Quant: INT4	Efficacy: 62/100	Energy: 0.9620J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_963	Params: 1936B	Quant: FP8	Efficacy: 63/100	Energy: 0.9630J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_964	Params: 1938B	Quant: INT4	Efficacy: 64/100	Energy: 0.9640J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_965	Params: 1940B	Quant: FP8	Efficacy: 65/100	Energy: 0.9650J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_966	Params: 1942B	Quant: INT4	Efficacy: 66/100	Energy: 0.9660J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_967	Params: 1944B	Quant: FP8	Efficacy: 67/100	Energy: 0.9670J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_968	Params: 1946B	Quant: INT4	Efficacy: 68/100	Energy: 0.9680J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_969	Params: 1948B	Quant: FP8	Efficacy: 69/100	Energy: 0.9690J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_970	Params: 1950B	Quant: INT4	Efficacy: 70/100	Energy: 0.9700J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_971	Params: 1952B	Quant: FP8	Efficacy: 71/100	Energy: 0.9710J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_972	Params: 1954B	Quant: INT4	Efficacy: 72/100	Energy: 0.9720J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_973	Params: 1956B	Quant: FP8	Efficacy: 73/100	Energy: 0.9730J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_974	Params: 1958B	Quant: INT4	Efficacy: 74/100	Energy: 0.9740J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_975	Params: 1960B	Quant: FP8	Efficacy: 75/100	Energy: 0.9750J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_976	Params: 1962B	Quant: INT4	Efficacy: 76/100	Energy: 0.9760J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_977	Params: 1964B	Quant: FP8	Efficacy: 77/100	Energy: 0.9770J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_978	Params: 1966B	Quant: INT4	Efficacy: 78/100	Energy: 0.9780J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_979	Params: 1968B	Quant: FP8	Efficacy: 79/100	Energy: 0.9790J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_980	Params: 1970B	Quant: INT4	Efficacy: 80/100	Energy: 0.9800J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n	Conf_981	Params: 1972B	Quant: FP8	Efficacy: 81/100	Energy: 0.9810J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_982	Params: 1974B	Quant: INT4	Efficacy: 82/100	Energy: 0.9820J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_983	Params: 1976B	Quant: FP8	Efficacy: 83/100	Energy: 0.9830J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_984	Params: 1978B	Quant: INT4	Efficacy: 84/100	Energy: 0.9840J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_985	Params: 1980B	Quant: FP8	Efficacy: 85/100	Energy: 0.9850J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Optimized	\n	Conf_986	Params: 1982B	Quant: INT4	Efficacy: 86/100	Energy: 0.9860J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_987	Params: 1984B	Quant: FP8	Efficacy: 87/100	Energy: 0.9870J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_988	Params: 1986B	Quant: INT4	Efficacy: 88/100	Energy: 0.9880J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_989	Params: 1988B	Quant: FP8	Efficacy: 89/100	Energy: 0.9890J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_990	Params: 1990B	Quant: INT4	Efficacy: 90/100	Energy: 0.9900J/t	Outcome: Sovereign	Friction: 3.0	ludo-Path: Optimized	\n	Conf_991	Params: 1992B	Quant: FP8	Efficacy: 91/100	Energy: 0.9910J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_992	Params: 1994B	Quant: INT4	Efficacy: 92/100	Energy: 0.9920J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_993	Params: 1996B	Quant: FP8	Efficacy: 93/100	Energy: 0.9930J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_994	Params: 1998B	Quant: INT4	Efficacy: 94/100	Energy: 0.9940J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_995	Params: 2000B	Quant: FP8	Efficacy: 95/100	Energy: 0.9950J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Optimized	\n	Conf_996	Params: 2002B	Quant: INT4	Efficacy: 96/100	Energy: 0.9960J/t	Outcome: Standard	Friction: 1.0	ludo-Path: Standard	\n	Conf_997	Params: 2004B	Quant: FP8	Efficacy: 97/100	Energy: 0.9970J/t	Outcome: Standard	Friction: 2.0	ludo-Path: Standard	\n	Conf_998	Params: 2006B	Quant: INT4	Efficacy: 98/100	Energy: 0.9980J/t	Outcome: Standard	Friction: 3.0	ludo-Path: Standard	\n	Conf_999	Params: 2008B	Quant: FP8	Efficacy: 99/100	Energy: 0.9990J/t	Outcome: Standard	Friction: 4.0	ludo-Path: Standard	\n	Conf_1000	Params: 2010B	Quant: INT4	Efficacy: 60/100	Energy: 1.0000J/t	Outcome: Sovereign	Friction: 1.0	ludo-Path: Optimized	\n