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acyildirimer/
Qwen3.6-27B-int4-AutoRound

Name: Qwen3.6-27B-int4-AutoRound
Availability: InStock

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Model details

This model is an INT4 quantized version of the original Qwen3.6-27B model.

Quantization was performed with AutoRound using a custom calibration dataset. The calibration set consists of 128 randomly selected samples from diverse categories, with sequence length 2048 and 300 optimization iterations.

The launch parameters below were tested on a headless RTX 3090 using the latest vLLM nightly build. MTP and prefix caching are confirmed to work.

vLLM launch example

bash

vllm serve acyildirimer/Qwen3.6-27B-int4-AutoRound \
  --host 0.0.0.0 \
  --port 8000 \
  --dtype auto \
  --kv-cache-dtype auto \
  --max-model-len auto \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder \
  --gpu-memory-utilization 0.96 \
  --enable-prefix-caching \
  --max-num-seqs 1 \
  --served-model-name Qwen3.6-27B \
  --language-model-only \
  --performance-mode interactivity \
  --attention-backend auto \
  --max-num-batched-tokens 4096 \
  --generation-config auto \
  --override-generation-config '{"temperature":0.6,"top_p":0.95,"top_k":20,"min_p":0.0,"repetition_penalty":1.0}' \
  --speculative-config '{"method": "mtp", "num_speculative_tokens": 2}'

Compatibility

The launch command was tested with the latest vLLM nightly build.
It should also work with the stable vLLM release, provided that the selected KV cache type is supported. If needed, use a supported --kv-cache-dtype value or set it to auto.
turboquant is not currently working on Ampere GPUs, so it is not enabled in the launch example above.

Troubleshooting

If you encounter out-of-memory errors, reduce --gpu-memory-utilization. For example, try 0.94, 0.92, or lower depending on your available VRAM and workload.
In some runs, vLLM may incorrectly calculate Available KV cache memory during startup. If this happens, terminate the running instance and launch it again with the same parameters.

Citation

Please cite both the original Qwen3.6-27B model and the AutoRound quantization method when using this quantized model.

Base model

bibtex

@misc{qwen36-27b,
  title  = {{Qwen3.6-27B}: Flagship-Level Coding in a {27B} Dense Model},
  author = {{Qwen Team}},
  year   = {2026},
  month  = {April},
  url    = {https://qwen.ai/blog?id=qwen3.6-27b}
}

Quantization method

bibtex

@article{cheng2023optimize,
  title   = {Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs},
  author  = {Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi},
  journal = {arXiv preprint arXiv:2309.05516},
  year    = {2023}
}