Qwen3.8-27B-AWQ-INT4

⚡ Qwen3.8-27B-AWQ-INT4

AWQ W4A16 quantization of Qwen/Qwen3.8-27B, produced with llm-compressor in compressed-tensors pack-quantized format.

Built and qualified with SGLang on Intel Arc Pro GPUs. The current Docker image supports both the checkpoint's MTP head and the external Qwen3.8-27B-DFlash2 draft model.

🧠 1,048,576-token context

The 1m revision contains the same weights as main. Only config.json changes: YaRN factor 4 extends the original 262,144-token configuration to 1,048,576 tokens.

from huggingface_hub import snapshot_download

snapshot_download(
    "ulkaa/Qwen3.8-27B-AWQ-INT4",
    revision="1m",
    local_dir="models/Qwen3.8-27B-AWQ-INT4-1m",
)

The current TP4 production recipe uses:

--context-length 1048576
--max-total-tokens 1048576
--max-mamba-cache-size 40
--max-running-requests 64

This produces an exact 1,048,576-token target pool with eight effective speculative request slots. Requests beyond active capacity queue. All active requests share the target pool, and each request's input plus requested output must fit the context limit. A full 1,048,576-token input leaves no room for generated tokens.

🚀 SGLang on Intel Arc Pro

Pull the model-scoped image:

docker pull rahulunair/sglang-xpu:qwen3.8-27b-20260819

The copy-paste TP4 commands, required container permissions, readiness checks, and full context notes live in the Qwen3.8-27B Docker Hub section. The moving model alias is rahulunair/sglang-xpu:qwen3.8-27b; use the dated tag above when reproducibility matters.

The image carries the qualified Intel XPU defaults for decode INT8, symmetric all-reduce, speculative verify, and graph capture. Do not add SGLang's CUDA custom collective; the published command uses --disable-custom-all-reduce so the XPU plugin owns the collective path.

🔮 Speculative decoding

Both supported modes use target verification. The draft proposes tokens; it does not bypass the target model.

MTP

The MTP head is inside this checkpoint and needs no second model. It is kept in BF16 and excluded from compressed-tensors quantization.

--speculative-algorithm EAGLE \
--speculative-num-steps 7 \
--speculative-eagle-topk 1 \
--speculative-num-draft-tokens 8

DFlash2

Download the external draft and mount it at /draft:

from huggingface_hub import snapshot_download

snapshot_download(
    "incoai/Qwen3.8-27B-DFlash2",
    local_dir="models/Qwen3.8-27B-DFlash2",
)

--speculative-algorithm DFLASH \
--speculative-draft-model-path /draft \
--speculative-num-draft-tokens 8 \
--speculative-dflash-block-size 8 \
--speculative-draft-window-size 2048

The published image includes the pinned DFlash2 model class and a compact physical draft-KV ring, so the target can retain a 1,048,576-token pool instead of allocating a second full-context draft pool. Radix prefix caching remains enabled. DFlash2 logprob-returning requests are rejected before scheduling because that request shape is not implemented.

🔬 What is quantized

Qwen3.8-27B is a hybrid: 48 of its 64 decoder layers use Gated DeltaNet (linear_attn.*) and 16 use full attention.

The three large DeltaNet projections, in_proj_qkv, in_proj_z, and out_proj, are quantized. Together they occupy 10.36 GiB in BF16 and 2.73 GiB in this artifact. The vision tower, MTP head, lm_head, embed_tokens, all norms, and the small DeltaNet scalar gates in_proj_a and in_proj_b remain BF16.

Activations remain BF16, hence W4A16. The artifact is deliberately larger than a maximally compressed four-bit build because the output head, embeddings, vision tower, MTP head, norms, and small gates stay at full precision.

🧪 Quantization and quality

• Source: Qwen/Qwen3.8-27B BF16, not the FP8 release.
• Method: AWQ via llm-compressor, W4A16 asymmetric, group size 128.
• Calibration: 512 sequences of 1,024 tokens, with 60% code instructions from codeparrot/self-instruct-starcoder and 40% general instructions from HuggingFaceH4/ultrachat_200k.
• Pipeline: sequential by Qwen3_5DecoderLayer, so each layer was calibrated against the quantized output of the preceding layers.

The group-128 choice was measured against group 32. Smaller groups add scale and zero-point metadata to every weight read. Prefill perplexity moved from 8.87 in BF16 to 9.33 after quantization.

Greedy text can diverge from BF16, as expected for a four-bit build. Evaluate the model on your own workload when exact BF16 behavior matters.

⚠️ Limitations

• The vision tower and processor configuration are preserved, but multimodal serving was only lightly exercised. The speculative recipe is qualified as a text-serving path.
• Calibration used text only. The unquantized vision tower was not calibrated.
• The 1M revision passed retrieval probes beyond the native context, but no graded long-context quality benchmark is claimed.
• DFlash2 does not currently support returned logprobs.

📄 License

Apache 2.0, inherited from the base model.