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malaiwah/
Qwen3.8-27B-EXL3-S16-V-research

Name: Qwen3.8-27B-EXL3-S16-V-research
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Research artifact, not a release: the first sub-4-bit build in this family, measured and rejected

This build was measured and rejected, and here is by how much. On shard 0 of the v5 held-out suite — 512 contexts, 1,048,064 scored positions, 330 source clusters, one shared BF16 head, one shared BF16 reference — it measures 0.045374 mean KL divergence, 95 % CI [0.041959, 0.049351], with top-1 agreement of 91.73 %. The decision rule was pre-registered before the conversion ran and keyed a NO to any mean above 0.030: this is 1.5x that threshold. Paired per context it loses 512 of 512 contexts to K4 — the worst previously published candidate — at +0.035028 [+0.032382, +0.038117], i.e. 4.39x its 0.010345, and 512 of 512 to the context edition at +0.041964 [+0.038790, +0.045661], i.e. 13.31x its 0.003409. All five strata lose to both comparators with every interval excluding zero. Do not deploy this checkpoint. It is published so that the rejection is auditable, because a NO nobody can check is an assertion.

If you want a build that runs, take malaiwah/Qwen3.8-27B-EXL3-K5K6-hydrated (the recommended build), the context edition for long windows, or K4 if you need the smallest published one.

Who should download this

Anyone auditing our NO. The 16 GB verdict in docs/34 rests on this number. With these bytes you can re-capture and re-replay it against the published shard-0 BF16 reference and check the interval, the tail row and the paired losses yourself, against the pre-registration that was committed and pushed before the conversion started.
Anyone extending the per-bit error law below 4 bits. This is the only sub-4-bit artifact in this family: 336 modules realised at K3, with the converter's own proxy error at that width, and the first evidence that the 3.73x-per-bit law holds one rung below where it was fitted (§ the law below its fit).
Not anyone looking for a serving recipe. This is not a small-VRAM build you should run. It is a rejected candidate, its fidelity is 4.4x worse than the worst thing we publish, and its tail is worse still. There is no configuration of it that we recommend, and none will be offered.

What was built

The docs/34 §6.1 primary 16 GB candidate — S16-V — converted exactly as specified:

role	modules	width	serialized bytes
`full_attention` q/k/v/o	64	K4 `mcg`	0.840 GB
`linear_attention`	144	K3 `mcg`	2.131 GB
`mlp_gate_proj`	64	K3 `mcg`	2.142 GB
`mlp_up_proj`	64	K3 `mcg`	2.142 GB
`mlp_down_proj`	64	K3 `mcg`	2.142 GB
`lm_head`	1	K4 `mcg`	0.636 GB
`mtp_draft`	8	K4 `mcg`	0.213 GB
`embed_tokens`	—	BF16 on disk	2.543 GB
`vision_tower`	—	BF16 (`-vb 16`)	0.921 GB
norms and small	—	BF16	0.001 GB

409 modules quantized, 400 of them body modules with a realised proxy error. Against the nearest measured neighbour, K4, this build is one bit lower on each MLP projection, three lower on the linear-attention stack (online K6 → K3), two lower on full attention (online K6 → K4) and two lower on the head (K6 → K4) — every one of them in the direction that had already made K4 the worst published candidate.

Realised per-role proxy error, median over the modules in the role, from the conversion log: full_attn (K4) 0.000724, linear_attn (K3) 0.003008, mlp_gate (K3) 0.004563, mlp_up (K3) 0.006575, mlp_down (K3) 0.009271, lm_head (K4) 0.000816. The eight MTP draft modules quantize through the uncalibrated fallback and report rmse (0.00238-0.00562) rather than this proxy.

The bytes, predicted to the byte

docs/34 §6.2 published a serialized payload prediction for this recipe before it was built: 13,711,503,428 B. The measured payload is 13,711,503,428 B — prediction error zero, and the first test of the affine byte law below 4 bits. Whole tree as built: 13,735,527,028 B over 21 files (12.7922 GiB), against the payload + 24.0 MB disk rule's 13,735,474,746 B, i.e. 52,282 B of tree-metadata slack on the disk figure and none on the payload. Every per-file digest is recorded in receipts/sixteen-flip-kld.json → build.tree and in this repo's SHA256SUMS. The published repository carries 23 files / 13,735,576,299 B: publishing adds README.md and DOCS-SHA256SUMS, which is the whole +49,271 B difference. The build figure is the one the byte law is tested against, so both are stated rather than one silently standing for the other.

The loader term, measured for the first time below 4 bits

vLLM's own gpu_model_runner reported 12.81 GiB of model-loading weights against this build's 12.7698 GiB payload, in 1.605 s: a loader delta of +0.0402 GiB, with the BF16 embedding resident (the fidelity protocol runs no int8 overlay). This is the first loader term ever measured for a sub-4-bit build. Beside the existing observations — +0.206 GiB on hydrated at 20.10 GiB, +0.305 to +0.342 GiB on the context build, +0.1067 GiB on K6-parity at 21.45 GiB — the term clearly scales with payload rather than being flat, so the +0.35 GiB planning allowance over-charges the 16 GB class by roughly 0.31 GiB, about a third of that class's entire KV pool.

The 16 GB class fails on fidelity, not on bytes

With the loader term measured instead of predicted, the int8-embedding resident-weight figure for this build is 11.626 GiB (payload 12,440,105,028 B = 11.5858 GiB, plus the measured 0.0402) against budgets of 12.70 GiB (docs/34 §3 basis) and 12.49 GiB (the qualified 0.955 utilisation basis) — 1.07 and 0.86 GiB of headroom. The weights fit, comfortably, on both bases.

So the 16 GB no-go does not rest on bytes; it rests on measured fidelity, and this card is that measurement. That is a harder and cleaner finding than the budget argument it replaces: the class is not out of reach because the artifact is too large, it is out of reach because the artifact that fits is 4.4x worse than the worst build we publish. What this does not settle: it is still a 32 GB board. The activation, non-torch and CUDA-graph terms in the 16 GB budget are carried from a 32 GB profile (docs/34 flip item 2), and no 16 GB board has been started in this project. Items (3) needle retrieval and text-plus-image at the 4.2 MP cap on a K3 body, and (4) the weighted non-termination check, are equally untouched.

The measurement

Shard 0 of the v5 suite, 512 contexts, 1,048,064 scored positions, suite token digest caef8a4628d6c07c162100895096f890cdf9cafc8e4c48b3d66035d737ee7cf7. Hidden state captured after the final norm and replayed through the shared BF16 lm_head (25a30fd5f826da0abc4efc4cc71def9f02bcb8085f7175eee284d221dee4cfff) against the BF16 reference capture. token_mean_kld, cluster bootstrap over 330 source clusters, 10,000 resamples.

build	mean KLD	median	p95	p99	p99.9	exact max	top-1
context edition	0.003409	0.001351*	0.010691*	0.035699*	0.164195*	3.749189	97.55 %
K4	0.010345	0.003204*	0.033157*	0.119353*	0.555521*	7.564510	95.91 %
S16-V (this build)	0.045374	0.013875	0.151928	0.543578	2.370397	12.503125	91.73 %

* the two comparators' percentiles are the histogram-bounded estimates in receipts/kld5-1M-tail-{k4,ctx}.json — exact to a bin width of 5.6 % — because no shard report carries the token-level vector an exact cumulative percentile would need. This build's row is exact, from its own shard-0 report; K4's exact shard-0 p99.9 is 0.5576.

The tail degrades with the mean, not faster than it. p99.9 is 2.3704 against K4's exact 0.5576, a 4.25x ratio versus the 4.39x mean ratio; the maximum rises 12.50/7.56 = 1.65x. The absolute tail is still substantially worse, which is why it was pre-registered, but the data do not support a superlinear-tail claim.

Paired per context, difference reported as comparator minus this build (positive means this build carries more KLD):

comparator	paired difference	95 % CI	contexts lost	ratio
K4	+0.035028	[+0.032382, +0.038117]	512 / 512	4.39x
context edition	+0.041964	[+0.038790, +0.045661]	512 / 512	13.31x

Per stratum, against the context edition: code +0.0505, literary +0.0507, multilingual +0.0424, encyclopedic +0.0346, scientific +0.0313. Against K4: literary +0.0430, code +0.0418, multilingual +0.0352, encyclopedic +0.0287, scientific +0.0265. Every interval excludes zero and no context in any stratum goes the other way.

One control closes the obvious escape. These reports are pairable against the published shard-0 reports because the reference capture was checked, not assumed: re-scoring the published hydrated checkpoint against this window's surviving BF16 reference reproduced the published mean bitwise (0.002699883159684943) with a paired difference of exactly 0.0 on all 512 contexts (receipts/sibling-rebuild-fidelity.json → capture_noise_floor). The comparison's floor is zero, not a tolerance.

The pre-registration held, including a miss worth printing

The prediction, the thresholds and the decision rule were committed and pushed in receipts/preregistration-kld9-window.json (0fa601b4fbc99bf9bb2d2a06f5941f52f8034a4d3858930d9fe3132a546f7c61, commit edced9a, 2026-08-16T15:05:21Z) before this conversion ran, so the verdict cannot have been chosen after the number was known.

registered	value	contains the measured 0.045374?
docs/34 §6.4 range	[0.030, 0.100]	yes
independent `sqrt_energy` surrogate bracket	[0.0318, 0.0585]	yes
primary point estimate from the byte law	0.0689	1.52x too high

The build is better than the byte-law point estimate and still nowhere near good enough. The direction and the size of that miss are printed here rather than reframed: the uniform-bpw form behind 0.0689 assumes error spreads in proportion to bytes, while this recipe moves five role groups at once, three of them by three bits.

The 3.73x-per-bit law, one rung below where it was fitted

The published ladder (receipts/error-driven-ladder.json) has no K3 rung for any module below 52M parameters, so the pre-registration extrapolated those 96 modules as eps(3) = eps(4) · eps(4)/eps(5). Comparing the converter's realised proxy error at the width it actually used against the laddered value for the same module at the same width:

modules	rung	ratio realised / laddered (median)	min	max
304	measured rung exists	0.9957	0.9731	1.0093
96	K3 extrapolated by the law	1.0164	0.9915	1.0455

So the per-bit law holds one rung below where it was fitted, to within about 4.5 % on the extrapolated modules. That is the reusable part of this artifact, and it is the reason to keep the bytes rather than only the receipt.

Reproduce this

bash

# 1. convert (~41 min on one RTX PRO 6000 Blackwell Server Edition)
export EXL3_BITS_FIXED='{"^.*self_attn\..*$": 4, "^.*linear_attn\..*$": 3}'
export EXL3_BITS_OVERRIDE='{"^model\.language_model\.layers\.[0-9]+\.mlp\.(gate|up|down)_proj$": 3}'
python convert.py -i /models/Qwen3.8-27B -o out/qwen38-s16v -w wd-s16v \
    -b 3 -hb 4 -mb 4 -vb 16 -cb mcg -cpi 1000000

# 2. finalize: index, quant config, verified topology, per-role bytes
python util/add_safetensors_index.py -m out/qwen38-s16v --force
python util/add_quant_config.py -m out/qwen38-s16v
tools/finalize_checkpoint.py -m out/qwen38-s16v --upstream /models/Qwen3.8-27B

# 3. measure against the published shard-0 BF16 reference (~6 min GPU to capture, ~3 to replay)
tools/fidelity.py capture --model out/qwen38-s16v --suite <suite>/shard-0000/suite \
    --out hidden/s16v --gpu-memory-utilization 0.85 --quantization exl3 --quantization-config "$QCFG"
tools/fidelity.py replay --reference <reference>/hidden-bf16 --candidate hidden/s16v \
    --head lm_head.safetensors --suite <suite>/shard-0000/suite --out reports/report-s16v.json
tools/fidelity.py paired --a reports/report-k4.json --a-label k4 \
    --b reports/report-s16v.json --b-label s16v --out reports/paired-k4-vs-s16v.json

The MLP override names the language-model body explicitly rather than the loose ^.*mlp\. form, because EXL3_BITS_OVERRIDE is matched against every key in f_targets and the MTP draft layer's three MLP projections are in there; the loose form would carry the draft to K3 as well and land 33,423,360 B below the published byte prediction for a recipe reason rather than a byte-law one. -cpi 1000000 disables periodic checkpointing: a first attempt with the default 120 s interval died at layer 4 with OSError: [Errno 38] Function not implemented: '/' from os.makedirs inside the checkpoint save, because under proot a transient stat failure made makedirs recurse to / and mkdir('/') returned ENOSYS instead of EEXIST. Checkpointing only writes and reads resume state, so with no interruption the quantization path is unchanged; the cost is that a crash restarts from scratch. Working directory peak: 12,790,298,239 B.

Converter: turboderp-org/exllamav3 @5f3c537ca9d89893d771256f5c43c93656553fbb (1.4.2) with this project's worktree diff 578066cd…. Hardware: 1x RTX PRO 6000 Blackwell Server Edition, SM120, driver 595.58.03 — no number on this card is comparable to one from a different card.

What that reproduces is the recipe, not this checkpoint. The EXL3 converter is nondeterministic in its .trellis payloads at identical widths, measured and quantified in receipts/converter-determinism.json and bounded in fidelity terms in receipts/sibling-rebuild-fidelity.json. Verify against this repo's SHA256SUMS, not against your own conversion, and read a byte difference below that floor as the converter rather than as tampering.

Files in this repository

the checkpoint: two safetensors shards, index, configs, tokenizer, chat template
SHA256SUMS — the immutable payload; DOCS-SHA256SUMS — every other file in the tree (this card, .gitattributes, crc32.txt and the flip/ artifacts), so a card edit cannot invalidate the build hashes
quantization_manifest.json and build-receipt.json — authoritative for composition, including the per-role byte totals quoted above; config.json → quantization_config keeps one bits/codebook pair for loader compatibility and cannot describe this mixed checkpoint
flip/build_s16v.sh and flip/convert-s16v.log — the build script and the full 1,053-line conversion log, including the realised bpw and proxy error of every module at K3. Both are digested in the receipt (toolchain.build_script, toolchain.convert_log) and neither survives anywhere else: the rental box they were produced on is being decommissioned

Evidence

receipts/sixteen-flip-kld.json — the verdict, the pre-registration read back out of its own file, the score and tail row, both paired reports and all five strata, the realised width and proxy error of every module, the byte accounting, the measured loader term, the 16 GB arithmetic, and the per-file digests of this tree
receipts/preregistration-kld9-window.json — the prediction, thresholds and decision rule, committed before the conversion
docs/34-vram-class-profiles.md — §6 is the 16 GB design study this build was made to test, and §6.4 now carries the measured answer
receipts/error-driven-ladder.json — the five-rung per-module error ladder this build extends one rung downwards
malaiwah/qwen38-27b-fidelity-suite-v5 — the suite, the shard-0 BF16 reference and the per-shard reports

What is not verified

No capability, task-retention, throughput, context-capacity, vision or safety evaluation was run on this build, and none will be: it is a rejected candidate kept for its measurement. The fidelity evidence is shard 0 only (512 contexts), not the ten-shard 10,480,640-position suite the released builds carry, and its 512-context hidden-state capture was released after replay, so re-checking the number from these weights means re-capturing it. It has never been served through the runtime for anything but hidden-state capture, and the 16 GB claims on this card are weight arithmetic on a 32 GB board, not a boot on a 16 GB one.

Like every EXL3 build in this family it requires the Gilded Gnosis vLLM fork with explicit --quantization exl3 and an exact ignore list; it does not load in upstream vLLM, SGLang, TensorRT-LLM, llama.cpp, transformers or stock exllamav3.

Prior art and credits

exllamav3 (Turboderp) — the EXL3 Trellis format, LDLQ calibration, the MCG codebook, the conversion pipeline, and the proxy_err this build's ladder work is built on.
Qwen — the base model.
Gilded Gnosis vLLM fork (Josh Cartu / jcartu) — the EXL3 serving path the capture ran through.
Research, recipe, harness and receipts: malaiwah/qwen38-27b-exl3.

malaiwah/Qwen3.8-27B-EXL3-S16-V-research