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/Qwen3-8B.

🚀 Quick Start

SGLang

DFlash is now supported on SGLang. And vLLM integration is currently in progress.

Installation

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

Inference

bash

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/Qwen3-8B \
    --speculative-algorithm DFLASH \
    --speculative-draft-model-path z-lab/Qwen3-8B-DFlash-b16 \
    --tp-size 1 \
    --dtype bfloat16 \
    --attention-backend fa3 \
    --mem-fraction-static 0.75 \
    --trust-remote-code

Transformers

This model requires trust_remote_code=True to load the custom architecture for block diffusion generation.

Installation

Ensure you have transformers and torch installed. Our evaluation is conducted with torch==2.9.0 and transformers=4.57.3.

pip install transformers==4.57.3 torch==2.9.0 accelerate

Inference

The following example demonstrates how to load the DFlash drafter and the Qwen3-8B target model to perform speculative decoding.

python

from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer

# 1. Load the DFlash Draft Model
# Note: trust_remote_code=True is required for the custom diffusion architecture. We recommend run on one GPU currently.
model = AutoModel.from_pretrained(
    "z-lab/Qwen3-8B-DFlash-b16", 
    trust_remote_code=True, 
    dtype="auto", 
    device_map="cuda:0"
).eval()

# 2. Load the Target Model
target = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen3-8B", 
    dtype="auto", 
    device_map="cuda:0"
).eval()

# 3. Load Tokenizer and Prepare Input
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")
prompt = "How many positive whole-number divisors does 196 have?"
messages = [
    {"role": "user", "content": prompt}
]
# Note: this draft model is used for thinking mode disabled
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# 4. Run Speculative Decoding
# The 'spec_generate' function is a custom method provided by the DFlash model
generate_ids = model.spec_generate(
    input_ids=model_inputs["input_ids"], 
    max_new_tokens=2048, 
    temperature=0.0, 
    target=target, 
    stop_token_ids=[tokenizer.eos_token_id]
)

print(tokenizer.decode(generate_ids[0], skip_special_tokens=True))

Evaluation

DFlash achieves up to 6.17x lossless acceleration for Qwen3-8B, making it nearly 2.5x faster than the state-of-the-art speculative decoding method EAGLE-3. Check out our GitHub repository to see how to reproduce the results.

Citation

If you find DFlash useful for your research or applications, please cite our project.

bibtex

@misc{chen2026dflash,
  title         = {DFlash: Block Diffusion for Flash Speculative Decoding},
  author        = {Chen, Jian and Liang, Yesheng and Liu, Zhijian},
  year          = {2026},
  eprint        = {2602.06036},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url           = {https://arxiv.org/abs/2602.06036}
}

shanjiaz/dflash-qwen3-8b

Qwen3-8B-DFlash-b16

🚀 Quick Start

SGLang

Installation

Inference

Transformers

Installation

Inference

Evaluation

Citation

shanjiaz/
dflash-qwen3-8b