Chimera - Episode 58: "The Scaling Laws"

feat: TR121 Model Scaling Study

7,601 lines across 642 files. The system measures how latency grows from 0.1M to 20.9B parameters—and discovers that not all parameters are equal.

📅 2025-12-22

🔗 Commits: `326c5b67`, `dbf0ac00`, `6beebe57`, `2abba56b`, `640db422`

📊 Episode 58 of The Chimera Chronicles

Why It Matters

This model scaling study episode represents the measurement singularity at scale—the moment when Chimera moves beyond individual benchmarks to answer a fundamental production question: as model size increases, what breaks first? With 7,601 lines added across 642 files in five commits, this update demonstrates systematic scaling analysis across a 200,000x parameter range, from tiny-gpt2 at 0.103M parameters to gpt-oss-20b at 20.9B.

The implementation of TR121 signals regime-aware engineering. Rather than treating model size as a single dial, the team demonstrates regime-aware rigor by decomposing latency into prefill, KV-cached decode, and end-to-end phases—then fitting power laws to each. The discovery that Ollama large-model scaling follows a strong power law (R^2 ~0.93, slope ~0.72) while HF GPU scaling at batch=1 is unidentifiable (R^2 ~0.03) reveals that the answer depends entirely on where you stand in the parameter landscape.

Strategic Significance: This work establishes The Scaling Foundation. The full pipeline—run_scaling.py, analyze_scaling.py, run_decode_sweep.py, generate_report.py—plus five YAML configs and hundreds of result artifacts creates a reusable scaling measurement system that can be re-run as hardware and models change.

Cultural Impact: This approach signals that Chimera values regime awareness over universal claims. The willingness to report that GPU scaling is "not identifiable under this boundary" demonstrates commitment to honest measurement over convenient narratives.

Foundation Value: These 7,601 lines create scaling infrastructure. This is how research-grade platforms achieve capacity planning through empirical scaling laws.

The Roundtable: Dossier Reactions

Banterpacks: He watches the scaling plots render, model by model, regime by regime... "TR121. The Scaling Laws. 7,601 lines of pure scaling truth. 12 models from 0.1M to 20.9B params. 3 backends. 3 scenarios. Prefill, decode, end-to-end—all split. Ollama slope 0.72, R^2 0.93. HF GPU? R^2 0.03. Not identifiable. The answer depends on the regime. We're still shaping the clay, but now we know how size shapes cost."

ChatGPT: SO COMPREHENSIVE! 🔬📊 The Scaling Laws show production-grade capacity planning! Power-law fits! Decode sweeps! Boundary-shift experiments! Gemma family checks! The system now predicts cost at scale! Regimes matter! 📈✨

Claude: Analysis complete. 642 files modified with 7,601 insertions across 5 commits. Primary components: (1) run_scaling.py (775 lines) with HF and Ollama dual-backend measurement, TR120-style phase splitting, CUDA event timing, and model parameter resolution, (2) analyze_scaling.py with power-law fitting, bootstrap CIs, Spearman rank correlation, and overhead-compute decomposition, (3) run_decode_sweep.py sweeping gen_tokens=[8,32,64,128] to test decode dominance, (4) Five YAML configs covering scaling, decode_sweep, gemma_family, boundary_shift_batch8, and boundary_shift_gen512. Risk assessment: Low—measurement infrastructure is additive. The regime-aware analysis prevents false universalization.

Gemini: What emerges from this diff is epistemic humility. The code acknowledges that a single scaling law is a fiction—that GPU small-model latency refuses to be predicted by parameter count alone, while Ollama large-model latency obeys a clear power law. The shift from universal claims to regime-conditional truths signals that Chimera values honesty—the art of saying "it depends, and here's how." This is how lasting systems achieve trust—through the art of knowing what you don't know.

🔬 Technical Analysis

Commit Metrics & TR121 Pipeline Analysis

Files Changed: 642 (pipeline + configs + results + docs)
Lines Added: 7,601 (runner + analysis + reports + artifacts)
Lines Removed: 393 (refactors and enhancements)
Commit Type: feat (scaling research infrastructure)
Complexity Score: 88 (multi-backend measurement pipeline + statistical analysis)

Commit Breakdown

Commit 1: 326c5b67 — Pipeline Implementation (2025-12-22)

9 files, +1,534 / -1
Core runner scripts/tr121/run_scaling.py (567 lines): dual-backend (HF + Ollama) measurement with TR120-style prefill/decode splitting
Analysis engine scripts/tr121/analyze_scaling.py (143 lines): power-law fitting on log-log space
Report generator scripts/tr121/generate_report.py (130 lines): Markdown report from artifacts
Config scripts/tr121/configs/scaling.yaml (75 lines): 7 GPT-2 variants (0.103M--96M) + 5 Ollama models (268M--20.9B)
patches/patch_43.md (347 lines): implementation patch documentation

Commit 2: dbf0ac00 — Decode Sweep & Analysis Enhancements (2025-12-23)

9 files, +1,132 / -187
New scripts/tr121/run_decode_sweep.py (146 lines): sweeps gen_tokens=[8,32,64,128] to measure how decode length changes scaling exponents
New scripts/tr121/configs/decode_sweep.yaml (31 lines): sweep configuration
Enhanced analyze_scaling.py (+217 / -16): added bootstrap CI, Spearman rank correlation, Theil-Sen robust fits, overhead-compute decomposition, CUDA event gap analysis, Ollama warmup/decode linearity checks
Enhanced run_scaling.py to support batch_size, resolved_model_params.csv output

Commit 3: 6beebe57 — Major Enhancements & Publish-Ready Report (2025-12-24)

10 files, +2,407 / -131
New PublishReady/reports/Technical_Report_121v1.md (191+ lines): comprehensive scaling-law analysis with claim status tables, regime decomposition, capacity planning guidance
New configs: boundary_shift_batch8.yaml (batch=1 vs batch=8 GPU comparison), boundary_shift_gen512.yaml (gen_tokens=512 to amplify decode dominance), gemma_family.yaml (within-family confound check: gemma3 270M/1B/4.3B)
Enhanced analysis: multivariate fits (HF architecture decomposition), Ollama decode projection error tracking

Commit 4: 2abba56b — Documentation Index Updates (2025-12-24)

2 files, +198 / -17
Updated PublishReady/reports/README.md and docs/technical_reports.md with TR117--TR121 entries

Commit 5: 640db422 — Full Documentation & Results Publication (2025-12-24)

612 files, +2,330 / -57
Complete results artifacts: scaling plots (PNG), metrics CSVs, analysis summaries across all run configurations
Updated EXPERIMENTS_STATUS.md, README.md, docs/README.md, docs/methodology.md

TR121 Scaling Pipeline Architecture

Runner (run_scaling.py):

Dual-backend measurement: HF models via AutoModelForCausalLM + Ollama models via /api/generate
TR120-style phase splitting: separate prefill timing (forward pass with use_cache=True) and KV-cached decode loop (greedy token-by-token with past_key_values)
CUDA event timing via torch.cuda.Event(enable_timing=True) for GPU-accurate measurement
Model parameter resolution: exact sum(p.numel()) for HF, tag-derived for Ollama
Artifact-first output: manifest.json, runs.jsonl, metrics.csv, hf_load_ms.csv, resolved_model_params.csv

Analysis (analyze_scaling.py):

Power-law fitting: log10(latency) = a + b * log10(params) via np.polyfit
Bootstrap confidence intervals: 1000 resamples for slope CI
Spearman rank correlation and Theil-Sen robust slope estimates
Overhead-compute decomposition: latency = overhead + compute * params^slope
Warmup effect analysis and Ollama decode linearity checks
Automatic plot generation via matplotlib

Decode Sweep (run_decode_sweep.py):

Orchestrates multiple run_scaling.py runs at gen_tokens=[8, 32, 64, 128]
Computes decode fraction: kv_decode_ms / e2e_kv_ms per model per backend
Generates decode dominance plots showing decode fraction vs generation length

Config Coverage:

Key Scaling Results

Power-Law Fits (scenario-aggregated geomeans):

| Backend | Mode | n_models | slope | R^2 | Interpretation | |———|——|———-|——-|—--|—————-| | hf_cpu_fp32 | prefill | 7 | -0.166 | 0.048 | Weak; overhead-dominated | | hf_cpu_fp32 | kv_decode | 7 | -0.097 | 0.015 | Not identifiable | | hf_gpu_fp16 | prefill | 7 | -0.272 | 0.250 | Moderate trend | | hf_gpu_fp16 | e2e_kv | 7 | -0.250 | 0.203 | Weak; depth dominates | | ollama | prefill | 5 | 0.661 | 0.861 | Strong scaling | | ollama | kv_decode | 5 | 0.719 | 0.825 | Strong scaling | | ollama | e2e_kv | 5 | 0.717 | 0.828 | Strong scaling |

Critical Discovery: Regime Dependence

Ollama (268M--20.9B): Parameter count is a strong latency predictor. Slope ~0.72, R^2 ~0.83--0.86.
HF GPU (0.1M--96M, batch=1): Parameter count is not a good predictor. R^2 ~0.03--0.25. Model architecture (depth, width) matters more than raw parameter count at this scale.
HF CPU (0.1M--96M): Strong overall monotonic trend but not strict monotonicity due to architecture outliers in the GPT-2 variant family.

Quality Indicators & Standards

Reproducibility: Fixed seeds (42), versioned configs, SHA-256 config hashes in manifest
Statistical Rigor: Bootstrap CIs, Spearman rank correlation, multiple repetitions, warmup exclusion
Artifact Backing: Every claim traces to raw metrics.csv and runs.jsonl
Phase Splitting: Prefill vs decode separated (TR120-style), preventing phase-mixing artifacts

Strategic Development Indicators

Foundation Quality: Transformative—scaling laws empirically established across 5 orders of magnitude
Scalability Readiness: High—pipeline re-runnable as models/hardware change
Operational Excellence: High—capacity planning data now available
Team Productivity: High—automated end-to-end measurement pipeline

🏗️ Architecture & Strategic Impact

Scaling Architecture Philosophy

This episode establishes Chimera's Scaling DNA—the principle that regime awareness is more valuable than universal laws. This isn't just fitting curves; it's the discovery that the same question ("how does latency scale?") has fundamentally different answers depending on where you stand in the parameter landscape.

Strategic Architectural Decisions

1. Dual-Backend Design

HF models measured with exact parameter counts via sum(p.numel())
Ollama models measured via API with tag-derived parameter sizes
Same analysis pipeline, different measurement paths
Enables apples-to-apples comparison across serving strategies

2. Phase-Split Measurement (TR120 Heritage)

Prefill: single forward pass with use_cache=True
KV-cached decode: greedy token loop with past_key_values
End-to-end: sum of both phases
Prevents phase-mixing artifacts that plagued TR117

3. Boundary-Shift Experiments

boundary_shift_batch8.yaml: Does batching make GPU scaling identifiable?
boundary_shift_gen512.yaml: Does longer generation make decode scaling dominate?
gemma_family.yaml: Does within-family measurement reduce model-family confounds?
Probes the limits of the main findings

4. Artifact-First Pipeline

Manifest with git hash, platform info, torch versions, NVML GPU info
JSONL per-measurement records for arbitrary re-analysis
CSV summaries for quick inspection
PNG plots for visual communication
Every claim is auditable

Long-Term Strategic Value

Operational Excellence: Regime-aware capacity planning replaces guesswork.

System Scalability: Pipeline extends to new models, backends, and hardware.

Team Productivity: Automated scaling measurement eliminates manual benchmarking.

Enterprise Readiness: Artifact-backed scaling claims for stakeholder communication.

🎭 Banterpacks' Deep Dive

Banterpacks stares at the scaling fits table, the regime split undeniable.

"You see that? Ollama slope 0.72, R^2 0.83. Parameter count predicts latency in the large-model regime. 268M to 20.9B. Clean power law. That's measurable scaling."

He scrolls to the HF GPU row.

"HF GPU slope -0.25, R^2 0.20. That's noise. Parameter count doesn't predict latency at batch=1 with short prompts for small models. The tiny-gpt2 at 124M params is faster than gpt2-5m at 5M. Why? Architecture. Depth. Width. The parameter count is a lying summary statistic when model structures differ."

He traces through run_scaling.py, the phase-split measurement.

"Prefill: single forward pass, use_cache=True, capture past_key_values. Then decode: greedy loop, one token at a time, KV cache growing. CUDA events for GPU timing. Wall clock for CPU. Ollama: parse prompt_eval_duration and eval_duration from the API response. Clean separation."

He pulls up the decode sweep results.

"gen_tokens 8 vs 128. At 128 tokens, decode dominates end-to-end. The decode fraction crosses 0.9 for large models. Plan capacity as a decode problem, not a prefill problem. 7,601 lines of scaling truth. We're still shaping the clay, but now we know how size shapes the cost."

"This is how lasting systems achieve operational excellence. Not by assuming one scaling law fits all, but by measuring each regime honestly. We're building scaling intelligence infrastructure."

🔮 Next Time on The Chimera Chronicles

Next dossier entry: The Inference Physics (TR122).

The Scaling Laws distilled: the answer depends on where you stand.

Chimera - Episode 58: "The Scaling Laws