Qwen3-Coder-Next-DFlash

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DFlash is a speculative decoding method that uses a lightweight block diffusion model to draft multiple tokens in parallel. This is the drafter model, which must be paired with Qwen/Qwen3-Coder-Next.

Quick Start

Installation

uv pip install "git+https://github.com/sgl-project/sglang.git@refs/pull/16818/head#subdirectory=python"

Launch Server

Use --speculative-num-draft-tokens to set the block size (8 or 16).

python -m sglang.launch_server \
    --model-path Qwen/Qwen3-Coder-Next \
    --speculative-algorithm DFLASH \
    --speculative-draft-model-path z-lab/Qwen3-Coder-Next-DFlash \
    --speculative-num-draft-tokens 16 \
    --tp-size 1 \
    --attention-backend fa3 \
    --mem-fraction-static 0.75 \
    --mamba-scheduler-strategy extra_buffer \
    --trust-remote-code

Tip: For long-context or agentic workloads, add --speculative-dflash-draft-window-size WINDOW_SIZE to enable sliding-window attention for the drafter.

Usage

from openai import OpenAI

client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")

response = client.chat.completions.create(
    model="Qwen/Qwen3-Coder-Next",
    messages=[{"role": "user", "content": "Write a quicksort in Python."}],
    max_tokens=4096,
)
print(response.choices[0].message.content)

vLLM

Community-contributed support is available. See PRs #36847 and #36767 for details.

Acceptance Length

• Max new tokens: 4096
• Block size: 16

  Dataset	Accept Length
  HumanEval	7.25
  MBPP	5.50
  LiveCodeBench	5.50

Acknowledgements

Special thanks to David Wang for his outstanding engineering support on this project. We are also grateful to Modal, InnoMatrix, and Yotta Labs for providing the compute resources used to train this draft model.

Citation

If you find DFlash useful, please cite our work. To share feedback on DFlash or request new model support, please fill out this form: DFlash Feedback.

@article{chen2026dflash,
  title   = {{DFlash: Block Diffusion for Flash Speculative Decoding}},
  author  = {Chen, Jian and Liang, Yesheng and Liu, Zhijian},
  journal = {arXiv preprint arXiv:2602.06036},
  year    = {2026}
}