The exact llama.cpp (qwen/4090) and vLLM (hermes/3090) launch commands,
recon'd 2026-05-20, so the foxhop-states salt states can be written
accurately rather than guessed. Notes the convention (salt manages the
systemd unit; engine binaries + model artifacts stay manual on
/mnt/data as documented prereqs) and the live-hermes cutover constraint
(keep >=1 hermes online; qwen is expendable).
Toward fox's next goal: score the full serving stack on quality AND
cost — {qwen, hermes} × {llama.cpp, vLLM} × {3090, 4090} × {solo,
arborist}, measuring CG% + GPU watts + CPU watts + joules/answer per
cell.
watt_bench.py — adds CpuSampler (Intel RAPL package energy via
/sys/class/powercap/intel-rapl:*/energy_uj). RAPL exposes a cumulative
microjoule counter, so energy-over-window is an end-minus-start diff
(handles wrap) — more accurate than integrating instantaneous power.
Sums multi-package. energy_uj is root-only by default (PLATYPUS /
CVE-2020-8694), so it degrades to available=False when locked;
--cpu-energy-cmd 'sudo cat {path}' supplies a privileged reader when a
sudo rule exists. Each cell now reports gpu/cpu/total joules-per-
question + gpu joules-per-token; the report records cpu_rapl_available.
Verified: graceful degradation when locked; RAPL diff math (1->4 MJ uJ
= 3.0 J, exact).
benchmark-matrix.md — expands the cost section to the full 16-cell
(model × engine × GPU × arm) design, the per-cell metric set (quality +
GPU + CPU energy), the serving-stack inventory from 2026-05-20 recon
(4090=qwen/llama.cpp, 3090=hermes/vLLM — each box has one engine + one
model today), and the buildout gap (vLLM+qwen, llama.cpp+hermes, cross-
GPU models). Notes idle-floor asymmetry (hermes/3090 ~127W vs
qwen/4090 ~20W) as a real optimizer input.
Harness is ready; the serving-config buildout + RAPL perm grant are the
remaining (ops, fox-directed) prerequisites to run the full matrix.
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.
