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@@ -135,6 +135,7 @@ One-shot investigations that don't fit the leaderboard format — claim verifica
 
 | Report | Finding |
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+| [NVFP4 on a 5090: real FP4 lands on the Qwen3.6-27B frontier where K-quants do](reports/nvfp4-frontier.md) · [chart](reports/chart_nvfp4_headtohead.png) · [frontier](reports/chart_nvfp4_frontier.png) | NVIDIA's NVFP4 is Blackwell's native 4-bit float; benchmarked as a GGUF rung on the exact Qwen3.6-27B K-quant frontier the quant-tax ladder measured, same harness (MMLU/GSM8K/HumanEval, greedy, think-off), one RTX 5090. It lands on the frontier: composite 92.11 (between Q5 and Q6), with MMLU (86.67) and GSM8K (97.60) identical to Q8_0 and Q6_K, and HumanEval one question under Q8. Clean measurement (zero empty outputs, no grader fallback). Two mechanisms. First, its decode sits on the memory-bound line: 78.6 tok/s from 18.1 GiB served, exactly where the K-quant curve predicts for that footprint (Q5 71.8 at 19.2 GiB, Q4 80.4 at 16.8), so Blackwell's FP4 dequant adds nothing measurable. That is the direct contrast with BTL-3-Compact's AVQ2, which is compute-bound and decodes slower than Q8. Second, it confirms NVFP4 is near-lossless: the NVFP4-vs-Q6 gap is 0.21pt on the 3-suite composite (June's t034 put it near a point on the 5-suite q_avg; the suite and build differ, both say near-lossless rather than cliff). The framing contrast: same rig and base, NVFP4 on the frontier, BTL-3's sub-2.5-bit 13pt below it. Honest limits: the 20.09 GB file carries an inert BF16 MTP draft head, so the single-stream served footprint is 18.1 GiB; the NVFP4-Q8 variant and the vLLM NVFP4-vs-AWQ throughput question were not run; 3-suite composite, one model. |
 | [BTL-3-Compact on a 5090: sub-2.5-bit runs, 13 points under plain Qwen3.6-27B](reports/btl3-compact.md) · [chart](reports/chart_btl3_frontier.png) | Bad Theory Labs' BTL-3-Compact is a sub-2.5-bit 27B (AVQ2, 8.4 GB) pitched as near-lossless and agentic, built on the exact Qwen3.6-27B this rig already quant-taxed. No prebuilt runtime ships for the card, so the AVQ2 llama.cpp fork was compiled from source for sm_120a: it runs, the first independent RTX 5090 validation of the format, with 27B resident in 9.5 GiB (a 27B fits that footprint only if the weights are compressed to it). On the same t090 harness (MMLU/GSM8K/HumanEval, greedy, think-off) it scores a 77.3 composite, 13 points below the K-quant ladder's lowest rung (Q3_K_M 90.5) at fewer bits, off the honest frontier its own base defines: per-suite 75.4/84.0/72.6 vs the ladder's 83.8/96.4/91.5 at Q3, measurement clean (zero empty outputs, no grader fallback). AVQ2 also breaks the K-quant rule that smaller is faster, decoding at 47 tok/s against Q8_0's 53 despite a third the bytes, because the mixed-precision unpack is compute-bound. The vendor's marquee numbers (95.12 HumanEval, 88.5 BFCL) are thinking-mode results, and the card discourages thinking as able to "become repetitive or fail to terminate"; a probe reproduced exactly that (most items exhaust a 2-3K token budget inside the reasoning block and never answer), with no published sampling, so the headline lives in a mode the vendor itself flags as unreliable and does not reproduce under greedy on a neutral harness. Honest limits: BTL-3-Compact is a finetune of the base, not the same weights re-quantized, so the gap is model-plus-method rather than the AVQ2 tax; the 92.2% retention claim (Compact vs full-precision BTL-3) and the agentic BFCL axis were not run. |
 | [The quant tax on Qwen3.6-27B: near-zero to Q4, one real step at Q3](reports/quant-tax.md) · [chart](reports/chart_quant_tax_5090.png) | The GGUF K-quant ladder the NVFP4 report kept deferring to: Q8_0 down to Q3_K_M, same subject, one pin, MMLU/GSM8K/HumanEval scored on the same harness every rung. Composite quality moves 92.3 to 92.0 from Q8 to Q4 (−0.4 pt, inside the noise) while single-stream decode climbs 52.8 to 80.4 tok/s (+52%) and the file shrinks 29.0 to 16.8 GB; Q3_K_M is the first rung past the noise floor, another +12% decode for −1.4 pt. Two operator reads: Q6_K strictly dominates Q8 (identical scores on all three suites, 6 GB smaller, 21% faster), and Q4_K_M is the sweet spot (80 tok/s, half a 5090's VRAM, within 0.4 pt of Q8). Only MMLU discriminates the top of the ladder (a gentle 86.7 to 84.6 slope Q8 to Q4); GSM8K and HumanEval sit flat until the Q3 step; no imatrix, so this is the low-rung floor. The perplexity column was cut and diagnosed, not hidden: the ladder's 34-chunk `-c 4096` sample gave a non-physical ordering (Q3 below Q8) that the standard `-c 512` stride (274 chunks) collapses to a Q8 = Q3 tie at ~7.05, so it was sampling noise, and at honest sampling wikitext2 barely separates these quants. Composite is a 3-suite mean, not a leaderboard q_avg. |
 | [First EAGLE-3 draft head for Hermes-4.3-36B: trained on one 5090, converged at 39% of the epoch, 1.29–1.57x](reports/hermes-drafter-epoch1.md) · [chart](reports/hermes-drafter-epoch1.png) | Hermes-4.3-36B (Seed-OSS architecture) had no speculative-decoding draft head anywhere on Hugging Face; this run trained the first one entirely on a single RTX 5090, using SpecForge's online mode with a 4-bit AWQ teacher (six memory patches, five filed upstream), 54K curated conversations, 54,000 steps across 8 unattended nights (~55 GPU-h, every night rc=0). Release bench (sglang 0.5.14, cuda graphs, 8 fixed prompts/workload, 66 tok/s no-drafter baseline): tree-3-4-8 wins all four workloads at 1.29–1.57x real decode speedup, while the wider tree-5-8-16 accepts deeper (accept-len to 2.22) yet lands slower everywhere — the draft cost eats more than the extra accepted tokens return. Two findings beyond the table: the head converged at step 21,000 (the epoch-end bench reproduces the 39%-of-epoch dry run within noise, so the last five nights of training moved nothing), and the 1.7–2.2x trained-head precedent band is out of reach at batch 1 on one card, where roughly a quarter of the theoretical accept-len gain pays for the draft pass itself. Decision recorded in the report: no epoch 2 on the same data; a v2 changes the data mix, not the step count. Weights + model card follow with the release. |