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@@ -128,6 +128,7 @@ One-shot investigations that don't fit the leaderboard format — claim verifica
 
 | Report | Finding |
 |---|---|
+| [FP4 on a consumer 5090: the Blackwell headline loses to plain int4](reports/fp4-consumer-blackwell.md) · [chart](reports/fp4-consumer-blackwell.png) | FP4 is the Blackwell selling point — benchmarked on one RTX 5090 (sm_120) against the quants you'd actually run, Qwen3-14B in vLLM 0.21. Two findings that compound. (1) NVFP4 is the only quant that won't run out of the box: AWQ/FP8 use prebuilt Marlin kernels, but NVFP4 makes FlashInfer JIT-compile native sm_120 FP4 cutlass kernels at load — needing ninja on PATH + a real CUDA toolkit + the correct CUDA_HOME (the default /usr/local/cuda-13.0 doesn't exist on the box) + a flashinfer-cache clear. (2) Once native FP4 is genuinely running (declared modelopt_fp4, not a Marlin dequant fallback), it's still slower than AWQ int4 at every batch: batch-1 decode AWQ 150 vs NVFP4 100 (0.66x) vs FP8 90; batch-32 AWQ 3937 vs NVFP4 3321. The 4-6x FP4 numbers are B200 tensor-core throughput; on consumer sm_120 a mature int4-Marlin kernel wins. bf16-14B doesn't fit 32GB (no KV room). Verdict: use AWQ int4, skip NVFP4 on consumer Blackwell. (QuTLASS / MR-GPTQ's real-FP4 4x claim, arXiv 2509.23202, is the parked follow-up.) |
 | [One-Shot EM doesn't reproduce on a 5090: entropy fell, accuracy didn't](reports/one-shot-em.md) · [chart](reports/one-shot-em.png) | One-Shot Entropy Minimization (arXiv 2505.20282) claims +24.7 on Qwen2.5-Math-7B from ONE unlabeled example in ~10 steps, no rewards. The full-param recipe OOMs on 32GB (14GB weights + 14GB bf16 grads), so the consumer-feasible version is LoRA (batch 16). Measured greedy pass@1 with the authors' grader: base reproduces the paper (MATH500 53.4 vs 53.0), but EM adds +2.0 at its peak step then collapses back by step 15, and AMC23 goes −2.5 (claim was +25.8 / +26.2). The keeper is the mechanism: the entropy objective trained fine (mean per-token entropy 0.098→0.035) while accuracy stayed flat — distribution-sharpening, not learning, in the paper's own words. Same base as Spurious Rewards (+21 on MATH500 from random rewards). Honest limits: full-param didn't fit, so this isn't a refutation of the multi-GPU number; the few-shot format control backfired on Qwen-Math's native zero-shot CoT. |
 | [Draft-free spec-decode on Qwen3-8B: workload split + dumb-vs-fancy null](reports/cacheback-spec-decode.md) · [chart](reports/cacheback-spec-decode.png) | Cache-only speculative decoding (no draft model, no extra VRAM) on Qwen3-8B: one greedy loop, three drafters (AR / one-line prompt-lookup / Cacheback's dynamic LRU table) × three workloads. The speedup is workload-shaped, not method-shaped — code 1.45-1.47x, summarize 1.30x, open chat 1.26x, with MAT tracking speedup 1:1, biggest where local agents live and no workload at zero. The null: Cacheback's LRU table ties one-line prompt-lookup (identical on 2/3 workloads, +0.01 MAT on code) — at leader-length 1 they are the same algorithm; Cacheback's real edge is its frozen corpus + tree drafting, not the dynamic table, and the cited "1.86x" is Vicuna-7B + frozen corpus on a 4090, not a modern 8B. Lossless: 46072/46080 tokens byte-identical to greedy; the 8 misses are exact bf16 logit ties (gap 0.000) where greedy itself is non-deterministic, not a decoder bug. |
 | [Sovereign TTS head-to-head: 1.7B Apache beats 4B research-license](reports/tts-head-to-head.md) · [chart](reports/tts-head-to-head.png) | Qwen3-TTS-1.7B (Apache) vs Fish-S2-Pro (4B, research license) on one RTX 5090, 150 Seed-TTS-eval EN utterances, both bf16 and neither compiled. Round-trip WER is a tie (0.6% each — Fish's sub-1% claim holds, Qwen matches it); SIM-o 0.699 vs 0.625; but RTFx 2.22× vs 0.39× and first-audio latency 1.72s vs 9.74s. Fish-S2-Pro's serving stack assumes SGLang + torch.compile + datacenter cards (its RTF<0.5 is an H200 number) — out-of-the-box on a consumer GPU the small open model is 5.7× faster at the same intelligibility. Size + serving assumptions, not quality. A compiled-Fish rerun is the obvious follow-up. |