Original model: Qwen/Qwen3.8-27B by Alibaba's Qwen team, Apache-2.0. This artifact contains modified weights (NVFP4 quantization); the original model is © its authors.

NVFP4-quantized variant of Qwen3.8 27B (27B). This quantized artifact exists first and foremost to run with the veloGB10 inference engine (gb10_inference) on NVIDIA DGX Spark / GB10 systems — it is produced, validated, and tuned for veloGB10's kernels. The weights use the standard HuggingFace compressed-tensors layout (nvfp4-pack-quantized), so this artifact can be used for any purpose, with any framework that reads the format. It is fit and proven to work with veloGB10 — greedy-lossless speculative decoding, two-node TP=2 serving, and the engine's correctness suite all pass on this artifact.

Note on modality: the underlying model is Qwen's hybrid Qwen3.5-family architecture and the checkpoint ships a vision tower (Qwen3_5ForConditionalGeneration). This artifact is currently presented as a text/language model — the vision (image/video) part is still being worked on and is not yet validated end-to-end. The language half (the dense GatedDeltaNet + GQA stack) is the tested, production path.

Specifications


Base model	Qwen/Qwen3.8-27B
Architecture	dense hybrid Qwen3.5 (48 GatedDeltaNet + 16 full-GQA layers), hidden 5120, 24 Q / 4 KV heads, head_dim 256
Attention	hybrid GatedDeltaNet (linear attention, fixed-size recurrent state) + periodic full GQA (every 4th layer)
MTP head	native multi-token-prediction head (used for speculative decoding)
Context	262,144 tokens
Vocabulary	248,320
Full-attention interval	4
Quantization	`nvfp4-full` (`recipe: all`) — all GEMMs NVFP4; the fastest recipe, the right 27B default at every context length
Format	`nvfp4-pack-quantized` (compressed-tensors compatible)
On-disk size	~16 GB

Running with veloGB10

Full instructions: veloGB10 README (build or download a release binary, then). The binary loads its PTX kernel artifacts from the current directory — run it from wherever the binary + *.ptx live.

This model is launched with the bundled Qwen3.8-27B DFlash2 drafter for speculative decoding (--spec-source dflash2-auto + --draft-dir). It uses the native MTP head alongside the drafter as the auto policy decides. Both TP=4 and TP=2 layouts are supported.

Four nodes — TP=4

bash

./gb10_inference --server \
  --model-dir /path/to/3.8-27b-nvfp4-full-all \
  --tp 4 \
  --nodes <peer-ip1>:29500, <peer-ip2>:29500, <peer-ip3>:29500 \
  --port 9000 \
  --max-seq-len 262144 \
  --max-batch 1 \
  --max-tokens 65536 \
  --prefix-cache on \
  --default-presence-penalty 1.5 \
  --mtp=auto \
  --spec-source dflash2-auto \
  --draft-dir /path/to/Qwen3.8-27B-DFlash2

(Three peer IPs listed because the head is the fourth node.)

Two nodes — TP=2

bash

./gb10_inference --server \
  --model-dir /path/to/3.8-27b-nvfp4-full-all \
  --tp 2 \
  --nodes <peer-ip>:29500 \
  --port 9000 \
  --max-seq-len 262144 \
  --max-batch 1 \
  --max-tokens 65536 \
  --prefix-cache on \
  --default-presence-penalty 1.5 \
  --mtp=auto \
  --spec-source dflash2-auto \
  --draft-dir /path/to/Qwen3.8-27B-DFlash2

You must also download the DFlash2 drafter (the speculative-decoding draft model used via --spec-source dflash2-auto). It is published at doth4580/Qwen3.8-27B-DFlash2 — grab it with e.g. hf download doth4580/Qwen3.8-27B-DFlash2 --local-dir /path/to/Qwen3.8-27B-DFlash2 (or via the web UI), then point --draft-dir at that local directory. If you only care about the model on its own you can omit --spec-source / --draft-dir and run without the drafter.

Measured on veloGB10 (greedy, NVFP4)

Single-stream decode, greedy, NVFP4 with DFlash 2. Figures are representative; real numbers vary with content type. Highest performance is on code generation. "Average" is a representative blend across content types (a code-heavy run averages much higher).

Mode	Average	Bottoms	Peaks	Max sustained (code)
Single node	> 40 tok/s	~11 tok/s	~100 tok/s	~70 tok/s
TP=2	~56 tok/s	~18 tok/s	~105 tok/s	~85 tok/s
TP=4	~85 tok/s	~32 tok/s	~150 tok/s	~125 tok/s

Averages span a wide range by content type: mixed-content runs average ~25 / ~38 / ~51 tok/s (single / TP=2 / TP=4) vs code-heavy sustained runs ~70 / ~85 / ~125 tok/s. Peaks are the top reached on code-generation content.

Life throughput traces and the benchmark methodology are in the veloGB10 README.

License & credits

Original model: Qwen/Qwen3.8-27B — © Alibaba Qwen team, Apache License 2.0. A copy of the license is included in this repository (LICENSE).
This artifact: quantized from the original with veloGB10's offline quantizer (nvfp4-full). Weights are modified relative to the original; tokenizer and chat template are unchanged from the original model.
Engine: veloGB10 (Apache-2.0).

doth4580/Qwen3.8-27B-NVFP4-FULL

Qwen3.8 27B — NVFP4 for veloGB10 (NVIDIA DGX Spark / GB10)