docs/research/local-model-adjudicator-sweep-2026-10-01.md

@@ -70,6 +70,16 @@ on GPU), context length (32k vs 4k made no difference), and the **inference engi
 of the same model measured 76 tok/s prefill and 8.6 decode, the same within noise. (Terse
 output saves only ~3.6 s of ~9.3 s because prefill dominates.) Two unrelated engines at the same
 speed points at the machine: ~140 GB/s effective memory bandwidth on a part rated for 800, and
-**iguana has macOS Low Power Mode on** (`pmset` `lowpowermode 1`). That is the leading suspect
-and is **not proven**: turning it off needs root and was not done. A first MLX benchmark was
+**iguana had macOS Low Power Mode on** (`pmset` `lowpowermode 1`). A first MLX benchmark was
 wrong because `mlx_lm.server` caches prompts; the figures above are from the corrected run.
+
+**Confirmed 2026-10-02.** The owner turned Low Power Mode off (`lowpowermode 0`, read back over
+ssh) and `gemma4:31b` was re-measured with nothing else resident: **217 tok/s prefill, 22.3
+tok/s decode** (4 runs, spread 22.2-22.6), against 103 and 7.9 with it on, so 2.1x and 2.8x. The
+slowness was the power mode, not the engine or the model. Caveats on the comparison: the new run
+used a different, synthetic Swedish prompt (~700 words, the original items held real transcript
+text and were deleted) and the model answered in 26-43 tokens, not the ~200 of the first run, so
+it is a same-machine before/after, not a controlled A/B. `gemma4:26b` and the MLX figures were
+not re-measured. The 31B's 97% judge accuracy is therefore usable at roughly a third of the
+latency the sweep implied; the 26B-vs-31B choice in ADR-034 was made on the slow numbers and is
+reopened by this.