Qwen3.5-27B-DFlash

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DFlash is a novel speculative decoding method that utilizes a lightweight block diffusion model for drafting. It enables efficient, high-quality parallel drafting that pushes the limits of inference speed.

This model is the drafter component. It must be used in conjunction with the target model Qwen/Qwen3.5-27B. It was trained with a context length of 4096 tokens.

Quick Start

Installation

vLLM:

uv pip install vllm
uv pip install -U vllm --torch-backend=auto --extra-index-url https://wheels.vllm.ai/nightly

SGLang:

uv pip install "git+https://github.com/sgl-project/sglang.git@refs/pull/20547/head#subdirectory=python"

Launch Server

vLLM:

vllm serve Qwen/Qwen3.5-27B \
  --speculative-config '{"method": "dflash", "model": "z-lab/Qwen3.5-27B-DFlash", "num_speculative_tokens": 15}' \
  --attention-backend flash_attn \
  --max-num-batched-tokens 32768

SGLang:

# Optional: enable schedule overlapping (experimental, may not be stable)
# export SGLANG_ENABLE_SPEC_V2=1
# export SGLANG_ENABLE_DFLASH_SPEC_V2=1
# export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1

python -m sglang.launch_server \
    --model-path Qwen/Qwen3.5-27B \
    --speculative-algorithm DFLASH \
    --speculative-draft-model-path z-lab/Qwen3.5-27B-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/Qwen3.5-27B",
    messages=[{"role": "user", "content": "Write a quicksort in Python."}],
    max_tokens=4096,
    temperature=0.0
)
print(response.choices[0].message.content)

Benchmark Results

Setup: Single NVIDIA B200, SGLang, thinking enabled, max output length 4096. We report end-to-end throughput, including prefill time. See our GitHub repository for reproduction scripts.

Throughput and Speedup

Tokens/sec (speedup vs. autoregressive baseline)

Block Size = 16

Task	Concurrency	AR	MTP	DFlash
Math500	1	84	243 (2.9x)	397 (4.7x)
	8	625	1457 (2.3x)	2270 (3.6x)
	16	1121	2224 (2.0x)	3135 (2.8x)
	32	1949	2504 (1.3x)	3712 (1.9x)
GSM8K	1	83	215 (2.6x)	330 (4.0x)
	8	625	1303 (2.1x)	1868 (3.0x)
	16	1109	1773 (1.6x)	2589 (2.3x)
	32	1914	2170 (1.1x)	3152 (1.6x)
HumanEval	1	83	236 (2.9x)	427 (5.2x)
	8	602	1345 (2.2x)	2079 (3.5x)
	16	1031	1921 (1.9x)	2748 (2.7x)
	32	1720	2234 (1.3x)	3198 (1.9x)
MBPP	1	84	200 (2.4x)	347 (4.2x)
	8	627	1049 (1.7x)	1826 (2.9x)
	16	1075	1729 (1.6x)	2479 (2.3x)
	32	1832	1933 (1.1x)	2808 (1.5x)
MT-Bench	1	84	169 (2.0x)	255 (3.0x)
	8	622	1035 (1.7x)	1444 (2.3x)
	16	1113	1550 (1.4x)	1984 (1.8x)
	32	1900	1772 (0.9x)	2391 (1.3x)

Block Size = 8

Task	Concurrency	AR	MTP	DFlash
Math500	1	84	273 (3.2x)	335 (4.0x)
	8	625	1673 (2.7x)	2020 (3.2x)
	16	1121	2731 (2.4x)	3646 (3.3x)
	32	1949	3739 (1.9x)	4288 (2.2x)
GSM8K	1	83	243 (2.9x)	301 (3.6x)
	8	625	1539 (2.5x)	1814 (2.9x)
	16	1109	2472 (2.2x)	2896 (2.6x)
	32	1914	3431 (1.8x)	3822 (2.0x)
HumanEval	1	83	258 (3.1x)	350 (4.2x)
	8	602	1486 (2.5x)	1856 (3.1x)
	16	1031	2302 (2.2x)	2749 (2.7x)
	32	1720	2477 (1.4x)	3412 (2.0x)
MBPP	1	84	234 (2.8x)	311 (3.7x)
	8	627	1375 (2.2x)	1757 (2.8x)
	16	1075	2159 (2.0x)	2661 (2.5x)
	32	1832	2885 (1.6x)	3309 (1.8x)
MT-Bench	1	84	210 (2.5x)	250 (3.0x)
	8	622	1300 (2.1x)	1495 (2.4x)
	16	1113	2105 (1.9x)	2403 (2.2x)
	32	1900	2873 (1.5x)	3256 (1.7x)

Acceptance Length

Format: MTP / DFlash (averaged across concurrency levels)

Task	B8	B16
Math500	5.73 / 5.90	7.14 / 7.93
GSM8K	5.54 / 5.57	6.84 / 7.22
HumanEval	5.81 / 6.34	7.38 / 9.18
MBPP	5.10 / 5.60	5.94 / 7.27
MT-Bench	4.60 / 4.54	5.30 / 5.47

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}
}