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@@ -220,6 +220,7 @@ One-shot investigations that don't fit the leaderboard format — claim verifica
 
 | Report | Finding |
 |---|---|
+| [Speculative decoding and prefix caching on one RTX 5090, Qwen3.8-27B Q6_K: the two free speedups on the model this box now serves](reports/spec-cache-study-qwen38.md) · [chart](reports/spec-cache-study-qwen38.png) | Rerun of the spec-cache study on Qwen3.8-27B Q6_K (the served model since 2026-09-13, own MTP head in the unsloth GGUF), same 304-request design, same flag set (llama.cpp b9653, ctx 64k, parallel 1, KV q8_0, batch 1). MTP n=2 lifts decode from 61 to 109-144 tok/s (1.8x prose and chat, 2.3x code and repetitive) for +9 ms TTFT; n=4 reaches 168-184 on code/repetitive and loses 5-8% on prose/chat; n-gram lookup is 1.00x. Prefix cache: TTFT 1.004 s to 0.069 s on a 2,954-token system prompt (15x), 3.814 s to 0.086 s on 11,570 tokens (44x), decode unchanged. Stacked, a short answer to the 11.5k context goes 4.25 s to 0.30 s. Losslessness: MTP byte-identical to plain greedy decode on 59% of answers; all 13 divergences are the target's own second choice at a near-tie (top-2 gap 0.004 to 0.133 nats, median 0.030). Every cell within noise of the Qwen3.6-27B run a week earlier: the speedups belong to the shape and the serving stack, not the generation. |
 | [How much thinking is worth paying for: GPQA-diamond think-on at three completion budgets](reports/reasoning-budget-curve.md) · [chart](reports/reasoning-budget-curve.png) | Two think-on models, the same 198 items, only `max_tokens` moved (4,096 / 8,192 / 16,384). Qwen3.8-27B Q6_K 60.1 / 70.2 / 79.3, Ornith 1.5 35B-A3B Q4_K_M 70.2 / 77.3 / 81.8. Every point lost at a smaller budget is a capped item (101 of 101 across the four smaller legs, per-item diff against 16k) and finished items are 94.7 to 96.1% correct at every budget, so the budget removes the hard questions rather than degrading the answers. 43% / 38% of items run past 4k, 34% / 30% past 8k, 22% of both still past 16k. Tokens per correct answer: Qwen 3,972 → 5,636 → 7,740, Ornith 3,248 → 4,732 → 6,996 (Ornith 700 to 900 cheaper at every budget, and 4.6x faster to decode). The 16k legs reproduced the August 16k runs on all 198 items. Limits: two models, one quant each, greedy, curve stops at 16k. |
 | [Speculative decoding and prefix caching on one RTX 5090: what the two free speedups buy](reports/spec-cache-study.md) · [chart](reports/spec-cache-study.png) | The two single-GPU optimisations every inference text names first, measured on the model and flag set this box serves daily (Qwen3.6-27B Q6_K with its embedded MTP drafter, llama.cpp b9653, batch 1, 304 requests, one variable at a time). MTP draft n=2 lifts decode from 61 to 112-144 tok/s (1.8x prose, 1.9x chat, 2.3x code and repetitive) for +10 ms TTFT; n=4 reaches 169-183 on code/repetitive and loses to n=2 on prose/chat, because draft depth only pays above ~0.85 acceptance; n-gram lookup is 1.00x on 256-token answers. Prefix caching cuts TTFT on a re-used 2,954-token system prompt from 1.005 s to 0.067 s (15x) and on 11,570 tokens from 3.835 s to 0.084 s (46x), decode untouched; stacked, a short answer to the long context goes 4.18 s to 0.24 s. Losslessness: MTP output is byte-identical to plain greedy decode on 56% of answers; all 14 divergences examined with top-2 logprobs are flips to the target's own second choice at a near-tie (gap under 0.09 nats), so same quality class, not byte-identical. Limits: one model, one quant, batch 1, 256-token answers. |
 | [The benchmark said below-chance. The model was fine: a truncation autopsy](reports/gpqa-truncation-artifact.md) · [chart](reports/chart_gpqa_truncation.png) | A community benchmark harness (SM12X-LLM-BENCH, full profile, 18h, rc=0, zero infra errors) reported Qwen3.6-27B Q6_K at 21.7% on GPQA-diamond — below the 25% random-guessing floor for 4-choice questions. The model was fine: the harness serves thinking models at their default (thinking on) with an 8,192-token completion cap, and on GPQA the model's reasoning chain was still running when the cap hit (ttft p50 184s), so 153/198 items (77%) ended `finish_reason=length` with no extractable answer, each scored as wrong. Anchor leg on the same GGUF file, same resident llama-server, same RTX 5090, thinking off so answers fit the budget: 54.5% (108/198, 0 parse failures) — 2.5x the score from one setting. Reads: below-chance accuracy on a competent model is a pipeline bug until proven otherwise; the truncation count was in the harness's own report all along, but the lane still presented as PASS/publishable, so score fields get read and diagnostic fields get skipped (guard proposed upstream as SM12X-LLM-BENCH#3, with #1 long-context detection and #2 cache-inflated prefill from the same run); thinking-mode benchmarks need deliberation-sized budgets, not answer-sized ones. Honest limits: anchor is think-off (lower bound proving artifact, not the model's think-on ceiling); one model, one quant, one lane; the harness's client plumbing was impeccable — the finding is about score presentation. |