Qwen3.8-27B-AWQ-4bit

AWQ W4A16 quantization of Qwen/Qwen3.8-27B, produced with llm-compressor in compressed-tensors pack-quantized format — the best-optimised Marlin path on Ampere (SM 8.6).

Built for 2× RTX 3090 running vLLM, but nothing here is 3090-specific.

Why this one is bigger than other 4-bit builds

Qwen3.8-27B is a hybrid model. Only 16 of its 64 decoder layers use full attention; the other 48 use Gated DeltaNet (linear_attn.*). Those DeltaNet projections are extremely sensitive to quantization, so they are left at full BF16 precision here, along with the vision tower and the multi-token-prediction head.

That means only 256 Linear modules are quantized — 16 layers × 4 attention projections plus 64 layers × 3 MLP projections — rather than the ~448 you would get by assuming a uniform architecture. Keeping 48 layers of DeltaNet in BF16 costs roughly 7 GB versus builds that quantize everything, and that is the entire difference in file size.

Everything protected here matches the exclusion set of the official FP8 release's modules_to_not_convert.

Usage

vLLM

vllm serve barrydeen/Qwen3.8-27B-AWQ-4bit \
  --trust-remote-code \
  --tensor-parallel-size 2 \
  --max-model-len 131072 \
  --gpu-memory-utilization 0.91 \
  --enable-prefix-caching \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_xml \
  --reasoning-parser qwen3 \
  --speculative-config '{"method":"mtp","num_speculative_tokens":3}'

Two things worth knowing:

• Use tensor parallelism, not pipeline parallelism, if you enable MTP. The MTP draft model does not implement SupportsPP, and startup fails with NotImplementedError: Pipeline parallelism is not supported for this model. Without MTP, --pipeline-parallel-size 2 is fine.
• If your host has no CUDA toolkit, set VLLM_USE_FLASHINFER_SAMPLER=0. FlashInfer's sampler JIT-compiles kernels and will fail with Could not find nvcc.

Transformers

from transformers import AutoModelForImageTextToText, AutoProcessor

model = AutoModelForImageTextToText.from_pretrained(
    "barrydeen/Qwen3.8-27B-AWQ-4bit", device_map="auto", trust_remote_code=True,
)
processor = AutoProcessor.from_pretrained(
    "barrydeen/Qwen3.8-27B-AWQ-4bit", trust_remote_code=True,
)

Measured performance

2× RTX 3090, vLLM 0.26.0, TP=2, MTP speculative decoding with 3 draft tokens:

Speculative acceptance by draft position: 85% / 69% / 47%.

Quantization details

• Source: Qwen/Qwen3.8-27B BF16 — not re-quantized from the FP8 release.
• Method: AWQ via llm-compressor, W4A16_ASYM, group size 128.
• Calibration: 512 samples from HuggingFaceH4/ultrachat_200k (train_sft), chat-template formatted, max_seq_length=1024.
• Pipeline: sequential, one decoder layer at a time (sequential_targets=["Qwen3_5DecoderLayer"]), so each layer is calibrated against the actual quantized output of the layers before it.

Excluded from quantization: linear_attn.* (Gated DeltaNet), the full visual.* tower, mtp.*, lm_head, embed_tokens, all norms, and the MoE gates.

Limitations and honest notes

• Calibration used 1024-token sequences, not 2048. Quantizing a 27B model alongside the sequential pipeline's activation cache exceeded available memory at 2048. Halving sequence length was chosen over halving sample count, on the reasoning that activation-statistic diversity across 512 distinct conversations matters more for AWQ than per-sample length. It does mean activations from the 1024–2048 token range were not observed during calibration.
• Calibration data is text-only. The vision tower is not quantized, so it is unaffected, but no image data was used.
• No formal benchmark suite has been run. The performance figures above are measured; quality has been verified only by inspection (coherent generation, correct arithmetic, working code output, no degradation under tool-calling). If you benchmark it, please open a discussion — particularly a comparison against builds that quantize the DeltaNet layers, since that is the central claim here and it deserves numbers rather than reasoning.

License

Apache 2.0, inherited from the base model.