Two deliverables for the cost/energy axis of the constraint optimizer.
docs/benchmark-matrix.md — shareable spec of the control experiment:
the question, fixture (386 office-holder Qs with corpus-vintage gold),
the 3-model × 3-framing × 2-arm matrix (18 cells), the verdict
vocabulary + two reads (accuracy vs grounding-fidelity), the
deterministic code judge + its Opus calibration, the results-so-far
table, and the NEW cost dimension (tokens / latency / GPU watts /
joules-per-answer measured per GPU tier). Self-contained — readable
cold by David.
bench/watt_bench.py — GPU wattage harness. Samples nvidia-smi
power.draw on the inference GPU while driving a small representative
subset, reports mean/peak watts, trapezoid-integrated joules,
joules-per-question, and joules-per-token. Tags the GPU
(--gpu-label 3090|4090) so the optimizer can compare hardware tiers.
Idle-baseline sampling separates load draw from idle. Does NOT grade
(energy is independent of correctness); saves answers + per-question
timing to JSONL for a later quality-per-joule pass via
score_with_code_judge.
Designed to run ON the GPU box (the orchestrator has no GPU; the
3090/4090 live on the inference boxes). Degrades gracefully when
nvidia-smi is absent (energy fields null) so it is testable anywhere.
Verified: PowerSampler graceful degradation + trapezoid integration
(synthetic 100->200->200W over 2s = 350 J, exact).
The headline cost finding the optimizer must weight: qwen-think
reasoning = 1300-3300 tokens/answer vs qwen-nothink ~50-100 (20-50x),
for a workload where arborist+qwen-nothink already lands 82% CG. The
energy numbers will quantify whether reasoning's premium is ever
justified — grounding-fidelity per joule, not per answer.
russell@unturf.com
5260161e6f