Chimera - Episode 55: "The Quantized Deep
docs: TR118v2.2 - Model Scale Comparative Analysis
Chimera - Episode 55: "The Quantized Deep"
docs: TR118v2.2 - Model Scale Comparative Analysis
1,327 lines. The system discovers the 76M crossover—and TensorRT's true power.
📅 2025-12-12
🔗 Commits: 92275d8, 7cfde87, 8bdddaf
📊 Episode 55 of The Chimera Chronicles
Why It Matters
This scaling research episode represents the parameter singularity—the moment when Chimera discovers exactly when CPU optimizations lose to GPU. With 1,327 lines in TR118v2.2, this update demonstrates frontier research execution and systematic scaling analysis.
The publication of TR118v2.2 signals deep empirical investigation. Rather than assuming GPU always wins, the team demonstrates systematic thinking by benchmarking across a 1,210x parameter span (0.103M to 124.4M). These 1,327 lines represent scaling intelligence that enables hardware-aware deployment decisions.
Strategic Significance: This work establishes The Crossover Point. The discovery that ONNX CPU advantage inverts at ~76M parameters provides concrete deployment guidance.
Cultural Impact: This approach signals that Chimera values quantified truth. The rigorous power-law fit and confidence intervals demonstrate commitment to statistical precision.
Foundation Value: These 1,327 lines create scaling knowledge. This is how research-grade platforms achieve optimal deployment through empirical analysis.
The Roundtable: Dossier Reactions
Banterpacks: He stares at the crossover plot, the power-law fit clear... "TR118. The Crossover. 1,327 lines of pure scaling truth. At 0.1M params, ONNX CPU is 21.9x faster than PyTorch. At 124M params, it's 32% slower. The lines cross at 76M. We're still shaping the clay, but now we know exactly where GPU becomes mandatory."
ChatGPT: SO PRECISE! 📈🔬 The Quantized Deep shows frontier-grade scaling analysis! 76M crossover! 2.96x TensorRT speedup! Profile mismatches discovered! The research now has scaling laws! Data drives decisions! 📊✨
Claude: Analysis complete. TR118v2.2 contains 1,327 lines with 720 benchmark runs. Key findings: (1) ONNX CPU vs PyTorch crossover at ~76M params (95% CI: 56M-120M), (2) TensorRT INT8 speedup: 1.35x at 0.1M → 2.96x at 124M, (3) All TensorRT generate runs fail with profile mismatch (not timeout), (4) GPT-2 perplexity delta <0.022%. The power-law fit (k=-0.506) is statistically robust.
Gemini: The diff reveals empirical humility. The code now understands that optimization is context-dependent. The shift from absolute to conditional truth signals that Chimera values nuance—the wisdom to know that "it depends." This is how lasting systems achieve efficiency—through the art of regime-aware optimization.
🔬 Technical Analysis
Commit Metrics & TR118 Report Analysis
- Files Changed: 3 (technical report + analysis scripts)
- Lines Added: 1,327 (comprehensive scaling analysis)
- Lines Removed: 0 (additive)
- Commit Type: docs (research publication)
- Complexity Score: 90 (high research depth)
TR118v2.2 Report Metrics
- Total Lines: 1,327
- Benchmark Runs: 720 (360 prefill + 360 generate)
- Models: tiny-gpt2 (0.103M) + GPT-2 (124.4M)
- Parameter Span: 1,210x
- Backends: 6 (PyTorch, ONNX CPU/GPU, TensorRT FP32/FP16/INT8)
Key Findings
The 76M Crossover:
| Model | Params | ONNX CPU | PyTorch | Ratio |
|---|---|---|---|---|
| tiny-gpt2 | 0.10M | 87,996 tok/s | 4,011 tok/s | 21.94x |
| gpt2-50m | 51.48M | 2,173 tok/s | 1,722 tok/s | 1.26x |
| gpt2-75m | 74.82M | 2,019 tok/s | 2,812 tok/s | 0.72x |
| gpt2 | 124.44M | 1,434 tok/s | 2,121 tok/s | 0.68x |
Power-Law Fit:
- k = -0.506
- Crossover at ~76M params
- 95% CI: 56M to 120M
TensorRT Scaling:
| Model | TRT INT8 vs PyTorch |
|---|---|
| tiny-gpt2 (0.103M) | 1.35x faster |
| GPT-2 (124.4M) | 2.96x faster |
- Scaling factor: 2.19x improvement as model grows 1,210x
- INT8 advantage increases with model size
Profile Mismatch Discovery:
- All TensorRT generate runs failed
- Error:
set_input_shape_failed: profile mismatch - Not timeouts—hard failures
- Root cause: Variable sequence lengths violate optimization profiles
Perplexity Preservation:
- GPT-2: All backends <0.022% delta from PyTorch
- tiny-gpt2: ~50,286 (matches uniform distribution over vocab 50,257)
- INT8 quantization: No accuracy loss detected
Production Deployment Matrix
| Model Size | CPU Option | GPU Option | Recommended |
|---|---|---|---|
| <1M params | ONNX CPU | ONNX GPU | ONNX CPU (20-100x faster) |
| 1M-10M | ONNX CPU | ONNX GPU | ONNX GPU (transition zone) |
| 10M-1B | ❌ | TRT FP16 | TRT FP16 |
| >7B params | ❌ | TRT INT8 | TRT INT8 |
Strategic Development Indicators
- Foundation Quality: Transformative—scaling laws now quantified
- Scalability Readiness: High—deployment matrix enables correct choices
- Operational Excellence: High—profile mismatch documented
- Team Productivity: High—clear decision framework
🏗️ Architecture & Strategic Impact
Scaling Architecture Philosophy
This episode establishes Chimera's Regime DNA—the principle that optimal varies with scale. This isn't just benchmarking; it's the discovery of phase transitions that enable regime-appropriate optimization.
Key Discoveries
1. The Crossover is Real
- ONNX CPU dominance doesn't last forever
- At 76M params, GPU takes over
- This is physics, not preference
2. TensorRT Scales Better
- 1.35x at tiny → 2.96x at 124M
- Graph-level optimizations compound
- Kernel fusion matters more as models grow
3. Profile Mismatches are Hard Failures
- TensorRT generate path broken
- Not a timeout—a shape mismatch
- Requires TRT-LLM for production decode
4. Perplexity is Preserved
- <0.022% delta proves correctness
- INT8 quantization doesn't hurt accuracy
- Production-safe deployment
Long-Term Strategic Value
Operational Excellence: Right backend for right scale.
System Scalability: Regime-aware optimization.
Team Productivity: Clear decision matrix.
Enterprise Readiness: Performance guarantees.
🎭 Banterpacks' Deep Dive
Banterpacks traces the crossover plot, finger following the power-law curve.
"You see that? At 0.1M params, ONNX CPU is 22x faster than GPU. At 75M params, it's 28% slower. The lines cross. That's not opinion—that's physics."
He points at the TensorRT scaling.
"1.35x speedup at tiny. 2.96x at 124M. TensorRT gets better as models get bigger. Kernel fusion compounds. Graph optimization pays dividends. That's scaling advantage."
He pulls up the profile mismatch errors.
"Every TensorRT generate run failed. Not timed out—failed with set_input_shape_failed. The optimization profiles don't cover decode shapes. We know exactly why TRT generate doesn't work yet. That's honest failure documentation."
He checks the perplexity table.
"<0.022% delta. That's not noise—that's precision preservation. We're not trading accuracy for speed. 1,327 lines don't scare me—they remind me we're still shaping the clay, but now we have the scaling laws."
"This is how lasting systems achieve operational excellence. Not by assuming, but by measuring across regimes. We're building scaling knowledge infrastructure."
🔮 Next Time on The Chimera Chronicles
Next dossier entry: The Cost of Thought (TR119).
The Quantized Deep distilled: scaling matters.