Measured DeepCFRMLP forward at bs={1,4,16,64,256,1024} on RTX 3090.
Per-state cost drops 232× from bs=1 (80 µs) to bs=256 (0.34 µs) while
per-call latency stays near 90 µs through bs=256. Policy-call supply
from a real run is ~368 states per traversal and ~200k per iteration,
well above the bs=64–256 plateau, so batched inference is not
supply-limited. GPU forward is not the limiter once batching exists.
Verdict: Optimization Priorities #5 (batched traversal inference) is
worth pursuing. End-to-end gain will still be bounded by encoding and
worker-GPU coordination overhead.
- scripts/profile_gpu_forward.py: standalone profiling script
- docs/performance.md: new "GPU forward profiling for batched traversal"
experiment section with table, supply estimate, and verdict
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
coolguyandClaude Opus 4.7
4014e49168