license: mit
library_name: transformers
pipeline_tag: text-generation
tags:
- dflash
- speculative-decoding
- diffusion
- efficiency
- flash-decoding
- qwen
- diffusion-language-model
Qwen3.5-27B-DFlash
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_SIZEto 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}
}