Qwen3.8-27B-PrismaScout-AQUA-Vision-20GB

Qwen/Qwen3.8-27B, compressed into a native multimodal checkpoint with a hard 20,000,000,000-byte artifact cap. It retains Qwen3.8 image and video input, its language-model head, and its multi-token-prediction (MTP) head.

The language body uses a fresh PrismaQuant full-AQUA per-Linear allocation for Qwen3.8: AURA weight-side damage plus activation-side pricing. Each of its 496 Linears has measured per-Linear AURA costs and a final NVFP4, FP8 E4M3, or BF16 assignment. Activation-changing body candidates are put onto an activation-inclusive cost scale with measured family-level estimator-transfer factors; this calibration is described precisely below. All 110 visual Linears are fixed to calibrated native NVFP4 W4A4. The single lm_head and all eight MTP Linears are fixed to measured FP8 E4M3. The body budget was solved around those fixed components so the complete multimodal checkpoint stays below the cap.

The result is a stock compressed-tensors checkpoint for vanilla vLLM: no vLLM fork, runtime plugin, or custom checkpoint loader is required.

Final recursive artifact-snapshot size: 19978899058 bytes (18.6068 GiB).
Language-body rate: 5.0737 bits per quantizable parameter over 24,350,556,160 body parameters. This bpp convention excludes fixed auxiliary components; the 20 GB cap includes every regular file in the published artifact snapshot. Hub-managed repository metadata is audited separately. The publisher retains the Hub-managed root .gitattributes (1,570 bytes), making the projected Hub tree total 19978900628 bytes; the publication wrapper verifies this accounting live.

Method, validation artifacts, and the rest of the PrismaQuant family: prismaquant.org

What is quantized

NVFP4 uses 4-bit weights and activations with group size 16 and calibrated positive global scales. FP8 E4M3 uses 8-bit weights and activations. The eight BF16 entries are the in_proj_a and in_proj_b linear-attention projections in language layers 1, 2, 4, and 13; the allocator chose to leave them at source precision. Embeddings, norms, biases, and other non-Linear residue remain at their declared source precision.

The visual format was constrained, not chosen by a zero-information proxy: all 110 visual Linears were rendered from image-conditioned calibration activations and bound to the exact source revision and calibration manifest. The language-body allocation remained fully empirical and per-Linear under the same whole-artifact budget.

Layer map

Key: violet = NVFP4 W4A4; teal = FP8 E4M3; orange = BF16; blank = that projection is not applicable at that position. The visual-tower inset covers its 27 transformer blocks and merger; the language-body panel covers all 64 layers, lm_head, and MTP.

Not rendered to scale. The PNG carries this disclaimer and its key inside the image. The project-site layer-map renderer generated it from the frozen 615-entry final allocation (layer_config.json SHA-256 beed93b6292116634bc66f60ab262121af0ee184e90bb4333ad069cacfe200c2), with exact 615/615 assignment coverage and no missing or extra qnames. The PNG SHA-256 is 7231dd83802a0a8df311ef5451d135b37c562cd13710c46529a05f40002ab5b4.

Measured

The core export and gold gates are bound by shipcard.json. The direct Qwen3.6 comparison and multimodal fixture campaign carry their own immutable result manifests, commands, input-contract or fixture hashes, and serving-stack fingerprints.

Text fidelity

KL and PPL are measured against the pinned BF16 Qwen3.8 source on identical corpus bytes and sequence boundaries. Mean KL is a screening measurement, not a complete behavioral guarantee; the tail is published rather than averaged away.

The KL run uses eight 512-token samples and scores all 4,088 non-initial positions against a pinned BF16 teacher payload. Each positional KL is computed over the teacher's top-1,024-token support plus one aggregated residual-tail bucket; it is not an exact token-by-token sum over the entire vocabulary. PPL uses 8,176 scored WikiText-2 test tokens in 16 non-overlapping 512-token chunks. The quantized and BF16 PPL arms share calibration-contract SHA-256 9f6e722ebc12b88e6b11466668f466c5cb15ae4b9ce001e856aee36a8efe63e6.

For context, the previously shipped Qwen3.6 PrismaScout now measures 0.0779318074 mean KL under the same deep 8 × 512, all-position protocol, using the same source corpus but its own tokenizer and pinned Qwen3.6 BF16 teacher. The token windows are therefore not byte-identical. This release's 0.0439023734 is 43.7% lower under that protocol. Qwen3.6 also measured 0.0513707392 on confident positions and 0.813661665 / 4.971227640 at p99 / max, versus this release's 0.0254206691 and 0.409348640 / 2.379668877. The measured deep-protocol tail therefore improves along with the mean.

An older Qwen3.6 release note reported roughly 0.0151, but that number came from an earlier 2 × 128 terminal-position sanity run that scored only two predictions. Its teacher payload, position coverage, and calibration depth were not bound to this contract. It is retained as historical context, not an apples-to-apples denominator.

The visionless Qwen3.8 PrismaScout-AQUA sibling measures 0.0402201669 under the same deep contract, so retaining vision under the same 20 GB cap raises this screening KL by 9.16%. The checkpoint also spends fixed bytes on native multimodality and FP8 output/MTP heads, leaving the language body at 5.0737 bpp. These are deliberate whole-model tradeoffs; activation-aware allocation does not guarantee that every aggregate text metric improves.

On the same PPL contract, this release's 9.648316858 is 0.852% lower than the visionless PrismaScout-AQUA sibling's 9.731222170 and 0.483% higher than the 5.5 bpp PrismaAQUA sibling's 9.601918337. These comparisons isolate neither vision nor allocation method by themselves; they describe the shipped whole-artifact tradeoffs.

Multimodal and runtime gates

Eager and CUDA-graph modes are validated in separate clean processes. The strict graph arm enables multimodal encoder graph capture explicitly and requires observed capture/replay statistics: mode 3, Inductor, FULL_AND_PIECEWISE, encoder budgets 64/256/1,024, maximum batch 2, and maximum four video frames. The association checks compare standalone, forward-batch, reverse-batch, and stale-replay token sequences; they are designed to catch image-cache or batching mistakes, not merely prove that a request returns HTTP 200.

The ship-gate PPL is a separate 583-token HTTP sanity suite, not the gold WikiText-2 PPL reported under Text fidelity. Because that gate deliberately disabled speculation, its mtp_acceptance check observed zero drafts and was skipped; MTP evidence comes from the separate eager and CUDA-graph generation arms above.

The eight-image suite is intentionally reported without inflating it into a general vision benchmark. The quantized artifact and BF16 source selected the same multiple-choice letter on every image, including the same one miss, in both deterministic replicates. Quantized repeat digest 96504b3ba76f5d6a11dd01bc2457479f5332a6e578b124e5a2762146274f9c1e was identical across repeats. Free-form captions were close paraphrases but 0/8 exact string matches, and one image received a materially different action interpretation. The fixture result is therefore choice parity on this suite, not broad caption equivalence.

On the warm second pass of that same 16-request visual mix, the quantized artifact decoded at 12.2990 tok/s versus 4.5573 tok/s for BF16 (2.699×), with mean TTFT 0.2627 s versus 0.5779 s, and mean end-to-end latency 1.4976 s versus 4.3904 s. Generated-token totals differed (259 versus 294), so these are observed workload results rather than a fixed-token decode microbenchmark. Warm-pass GPU utilization averaged 95.91% (p50/p95/max 96%) at 34.01 W mean reported GPU power on DGX Spark.

Serving performance

This exact-artifact text-path campaign compares the previously shipped Qwen3.6 PrismaScout directly with this release. Both artifacts ran on the same physical NVIDIA GB10, driver 595.84, pinned runtime image, and matched vLLM flags. Both used the same Qwen3.6 tokenizer, fixed random prompts and seeds, and --language-model-only --skip-mm-profiling to isolate language serving. The Qwen3.6 result files were reused immutably from the matched prior run; their result-set identity was revalidated before analysis. Ratios below are the Vision artifact's repeat mean divided by the Qwen3.6 repeat mean. Power mode was not pinned or matched, so this is not a power-efficiency comparison. The frozen 48-file receipt and analyzer output are identified by hashes in Provenance.

No-spec prefill: eight requests per repeat, each exactly 2,048 input tokens and one output token. Input throughput is the primary metric; n=3 per arm.

Decode: four requests per repeat, each exactly 32 input and 256 output tokens. Output throughput is the primary metric; n=3 per arm without speculation and n=6 per arm with MTP-3.

All 48 observed result JSONs passed identity, request, token, failure, metric, and throughput-arithmetic checks, and all 12 groups were complete. Primary throughput CVs are shown rather than hidden. All no-spec primary-throughput rows stayed below their CV thresholds. Both models' MTP rows exceeded the campaign's 3% (c=1) or 5% (c=4) CV thresholds; MTP TTFT CV reached 21.51%, and the Vision no-spec c=1 TTFT CV was 8.54%. Across primary, derived, and latency metrics, the analyzer records 27 CV disclosure flags and therefore sets strict_pass=false. The MTP point estimates are informative repeated means, not a clean CV-gated performance pass or a claim of statistical significance.

Within this contract, no-spec prefill is within −1.49% to +0.38% of Qwen3.6, no-spec decode is 0.95% to 4.01% faster, and MTP-3 decode is 23.27% to 26.65% faster. The previous release appears only as a serving comparator; its quantization process is not part of this checkpoint's build.

How it was built

AURA supplies per-Linear language-body costs from a fresh Qwen3.8 KL-adjoint probe and the production-rendered weight delta—the same representation later exported. The allocator had exact probe and cost coverage for all 496 body Linears, the output head, and eight MTP Linears.

For the body, this run's AQUA activation correction is calibrated at the format-family level. Eight MTP rows carry both activation-inclusive measured output MSE and the corresponding weight-only estimator for each family. Their geometric-mean ratios produce estimator-transfer factors of 8,102.699× for NVFP4 and 7,658.832× for FP8. Those factors put the 496 per-Linear AURA body rows for each activation-changing family onto an activation-inclusive cost scale before the multi-choice solve. This is not a claim that all 992 NVFP4/FP8 body candidates had direct activation-output measurements: their weight damage is measured per Linear, while the activation correction is the receipt-bound family transfer described here. The eight BF16 body candidates selected by the solver remain exact activation-identity rows.

Vision uses a different, exact calibration path. All 110 fixed-NVFP4 visual Linears were rendered from image-conditioned activation captures with exact per-Linear activation widths and positive finite global scales. The visual format was fixed to native NVFP4 W4A4; it was not selected from an uninformative proxy.

The multi-choice budget solve selects a receipt-bound feasible language-body assignment across {NVFP4, FP8_E4M3, BF16} while the visual tower, lm_head, and MTP formats are fixed auxiliary assignments that still count toward the hard whole-artifact cap. It does not claim global optimality.

The production render uses GPTQ with fixed damping 1.0, static activation ordering, and joint scale optimization (JSO). The visual tower uses that native NVFP4 W4A4 render path with image-conditioned calibration activations and calibrated input/weight global scales. It is not a separately loaded sidecar.

The two-machine build was striped by complete Linear ownership, rendered into the existing resident production-weight cache, and combined by an exact disjoint union. Final export fails closed unless all 607 quantized Linear cache pairs are resident and receipt-bound. The remaining quantized language-body, lm_head, and MTP weights then passed through the existing production recache: the quantized upstream weights were installed during replay and downstream activation maximums were re-fit before export. The 110 image-conditioned visual scales were explicitly preserved because the text replay cannot observe them. Quantization itself does not use tensor parallelism; each Linear has exactly one rendering owner.

Serving with vLLM

The validated lane is vanilla vLLM with native compressed-tensors kernels on Blackwell. The final runtime fingerprint and exact gate commands are recorded in the release evidence. This conservative example uses the gated 4,096-token configured serving ceiling:

vllm serve rdtand/Qwen3.8-27B-PrismaScout-AQUA-Vision-20GB \
  --host 0.0.0.0 --port 8000 \
  --quantization compressed-tensors \
  --max-model-len 4096 \
  --max-num-seqs 2 \
  --gpu-memory-utilization 0.75 \
  --kv-cache-dtype fp8 \
  --enable-prefix-caching \
  --safetensors-load-strategy prefetch \
  --limit-mm-per-prompt '{"image":1,"video":1}' \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_xml

• Add --speculative-config '{"method":"mtp","num_speculative_tokens":3}' for the separately validated MTP text-generation path.
• Adjust --limit-mm-per-prompt to match your workload and memory budget.
• The source model supports a native 262,144-token context. This release is served and gated with a configured 2,048-token ceiling in the strict multimodal/MTP checks and a configured 4,096-token ceiling in the visual-quality and direct-throughput campaigns. Those ceilings are not claims that maximum-length prompts were exercised. Longer contexts require their own memory and quality validation.
• Thinking mode is on by default. --reasoning-parser qwen3 separates the thinking block from response content; qwen3_xml matches the model's tool call format.

Example image request through the OpenAI-compatible endpoint:

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="not-used")
response = client.chat.completions.create(
    model="rdtand/Qwen3.8-27B-PrismaScout-AQUA-Vision-20GB",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}},
                {"type": "text", "text": "Describe the image."},
            ],
        }
    ],
)
print(response.choices[0].message.content)

Limitations

• Native NVFP4 W4A4 serving is hardware- and kernel-dependent. This artifact is validated on NVIDIA GB10 / DGX Spark with the pinned vLLM container below; unlisted GPU architectures and fallback kernels are not claimed.
• Quantization can change rare-token, tool-call, OCR, fine-detail, and long-context behavior. Mean KL and a small multimodal fixture suite cannot cover every failure mode.
• The visual calibration set contains eight pinned COCO-caption images. Final validation uses a disjoint eight-image test set plus real association, resolution, batching, and video fixtures. Behavior far from those domains is not established.
• The 5.0737 bpp figure covers quantizable language-body parameters only. Use the exact recursive artifact bytes—not bpp—when comparing storage budgets.
• Body activation distortion is calibrated through one measured estimator-transfer factor per format family. Per-Linear body weight damage is measured, but the body does not carry a direct activation-output measurement for every (Linear, format) row. This residual modeling limit is one reason the card publishes deep end-to-end KL and PPL.
• MTP is optional. Its eager and graph text-generation arms passed, but the strict speculative test did not combine MTP with image/video requests.
• The eight-image suite matched BF16 multiple-choice predictions but not exact captions. Treat 7/8 on this fixture as a smoke-scale result, not a general visual benchmark.
• This is a quantized derivative, not a retraining or fine-tune. Refer to the Qwen3.8-27B source card for the base model's capabilities, usage guidance, license, and benchmark scope.

Provenance

The final shipcard.json is the authority. If a value in this README differs from that machine-verifiable record, treat this README as stale.

The text-only 20 GB sibling is available at rdtand/Qwen3.8-27B-PrismaScout-AQUA-20GB, and the 5.5 bpp PrismaAQUA sibling is available at rdtand/Qwen3.8-27B-PrismaAQUA-5.5bit-vllm. The serving comparator is the previously released Qwen3.6-27B-PrismaSCOUT-Blackwell-NVFP4-BF16-vllm; it remains a supported choice for users who prefer Qwen3.6.

• Project: https://prismaquant.org
• Code: https://github.com/RobTand/prismaquant
• Base model: https://huggingface.co/Qwen/Qwen3.8-27B

Citation

@software{tand_prismaquant,
  author  = {Tand, Robert},
  title   = {PrismaQuant: production-faithful KL--Fisher bit allocation},
  url     = {https://prismaquant.org}
}

Contact: robert.tand@icloud.com