Qwen3-8B-DFlash-b16
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
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.
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.
@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}
}