Qwen3-Coder-30B-A3B-DFlash

Paper | GitHub | Blog

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-Coder-30B-A3B-Instruct.

📊 Training Data & Efficiency

Qwen3-Coder-30B-A3B-DFlash is trained on 289K samples, composed of:

• Code split from nvidia/Nemotron-Post-Training-Dataset-v2
• theblackcat102/evol-codealpaca-v1
• Approximately 2.8K Cline execution traces collected by ourselves

Despite being trained on significantly less data, DFlash already outperforms EAGLE-3 in inference acceleration. In comparison, lmsys/SGLang-EAGLE3-Qwen3-Coder-30B-A3B-Instruct-SpecForge is trained on the open-perfect-blend dataset with 1.4M samples, nearly 5× more data than DFlash.

This result highlights the training efficiency and scalability of DFlash, and suggests that further scaling the training data can unlock even greater acceleration gains.

🚀 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/16818/head#subdirectory=python"

Inference

python -m sglang.launch_server \
    --model-path Qwen/Qwen3-Coder-30B-A3B-Instruct \
    --speculative-algorithm DFLASH \
    --speculative-draft-model-path z-lab/Qwen3-Coder-30B-A3B-DFlash \
    --tp-size 1 \
    --dtype bfloat16 \
    --attention-backend fa3 \
    --mem-fraction-static 0.75 \
    --trust-remote-code

Transformers

Installation

pip install transformers==4.57.3 torch==2.9.0 accelerate

Inference

from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer

model = AutoModel.from_pretrained(
    "z-lab/Qwen3-Coder-30B-A3B-DFlash", 
    trust_remote_code=True, 
    dtype="auto", 
    device_map="cuda:0"
).eval()

target = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen3-Coder-30B-A3B-Instruct", 
    dtype="auto", 
    device_map="cuda:0"
).eval()

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-Coder-30B-A3B-Instruct")
prompt = "Please provide a Python implementation of the Bubble Sort algorithm."
messages = [
    {"role": "user", "content": prompt}
]
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)

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 consistently achieves high acceptance lengths and speedups across different concurrency levels. DFlash achieves similar acceptance length for both the bfloat16 target model and its FP8 variant Qwen3-Coder-30B-A3B-Instruct-FP8. All experiments are conducted using SGLang on a single B200 GPU.

We use a block size of 16 during speculation. You can specify different block size during inference by passing --speculative-num-draft-tokens arguments when launch the server.

HumanEval

Batch Size	Method	Output Throughput (tokens/s)	Acceptance Length	Speedup vs. AR
1	Autoregressive	229	1.00	1.00×
1	DFlash	802	8.09	3.5×
4	Autoregressive	686	1.00	1.00×
4	DFlash	2078	8.09	3.0×
8	Autoregressive	1068	1.00	1.00×
8	DFlash	3442	8.09	3.2×
16	Autoregressive	1681	1.00	1.00×
16	DFlash	5429	8.09	3.2×
32	Autoregressive	2713	1.00	1.00×
32	DFlash	8314	8.09	3.1×

---

MBPP

Batch Size	Method	Output Throughput (tokens/s)	Acceptance Length	Speedup vs. AR
1	Autoregressive	228	1.00	1.00×
1	DFlash	720	7.23	3.2×
4	Autoregressive	682	1.00	1.00×
4	DFlash	2052	7.23	3.0×
8	Autoregressive	1057	1.00	1.00×
8	DFlash	3360	7.23	3.2×
16	Autoregressive	1697	1.00	1.00×
16	DFlash	5522	7.23	3.3×
32	Autoregressive	2735	1.00	1.00×
32	DFlash	8538	7.23	3.1×

---

LiveCodeBench

Batch Size	Method	Output Throughput (tokens/s)	Acceptance Length	Speedup vs. AR
1	Autoregressive	220	1.00	1.00×
1	DFlash	569	6.42	2.6×
4	Autoregressive	681	1.00	1.00×
4	DFlash	1621	6.42	2.4×
8	Autoregressive	1112	1.00	1.00×
8	DFlash	2554	6.42	2.3×
16	Autoregressive	1733	1.00	1.00×
16	DFlash	4160	6.42	2.4×
32	Autoregressive	2823	1.00	1.00×
32	DFlash	6401	6.42	2.3×

Acknowledgement

We are grateful to Yotta Labs for their compute support in training this draft model.

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}
}