gemma-4-31B-it-DFlash

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DFlash is a speculative decoding method that uses a lightweight block diffusion model to draft multiple tokens in parallel. This is the drafter model, which must be paired with google/gemma-4-31B-it.

Quick Start

Installation

vLLM: until Gemma4 DFlash support is merged, install vLLM from PR #41703:

uv pip install -U --torch-backend=auto \
  "vllm @ git+https://github.com/vllm-project/vllm.git@refs/pull/41703/head"

SGLang:

uv pip install "git+https://github.com/sgl-project/sglang.git@refs/pull/23000/head#subdirectory=python"

Launch Server

vLLM:

vllm serve google/gemma-4-31B-it \
  --speculative-config '{"method": "dflash", "model": "z-lab/gemma-4-31B-it-DFlash", "num_speculative_tokens": 15, "attention_backend": "flash_attn"}' \
  --attention-backend triton_attn \
  --max-num-batched-tokens 32768 \
  --trust-remote-code

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 google/gemma-4-31B-it \
  --speculative-algorithm DFLASH \
  --speculative-draft-model-path z-lab/gemma-4-31B-it-DFlash \
  --speculative-num-draft-tokens 16 \
  --tp-size 1 \
  --attention-backend triton \
  --speculative-draft-attention-backend fa4 \
  --trust-remote-code

Usage

For vLLM, use port 8000. For SGLang, use port 30000.

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")

response = client.chat.completions.create(
    model="google/gemma-4-31B-it",
    messages=[{"role": "user", "content": "Write a quicksort in Python."}],
    max_tokens=4096,
    temperature=0.0,
    extra_body={"chat_template_kwargs": {"enable_thinking": True}},
)
print(response.choices[0].message.content)

Benchmark Results

Setup: Single NVIDIA B300 GPU per server/run, vLLM, thinking enabled, max output length 4096, greedy decoding.

Throughput and Speedup

DFlash achieves up to 5.8x speedup at concurrency 1.

Generated tokens/sec (speedup vs. autoregressive baseline)

Block Size = 16

Task	Concurrency	AR	DFlash
Math500	1	77	447 (5.8x)
	8	511	2650 (5.2x)
	32	1308	4962 (3.8x)
GSM8K	1	78	408 (5.3x)
	8	520	2321 (4.5x)
	32	1382	4447 (3.2x)
HumanEval	1	76	420 (5.6x)
	8	494	2389 (4.8x)
	32	1145	4139 (3.6x)
MBPP	1	79	343 (4.4x)
	8	535	2036 (3.8x)
	32	1389	3636 (2.6x)
MT-Bench	1	79	236 (3.0x)
	8	503	1334 (2.7x)
	32	1177	2257 (1.9x)

Acceptance Length

Task	c1	c8	c32
Math500	8.59	8.59	8.62
GSM8K	7.53	7.50	7.52
HumanEval	8.00	7.89	7.96
MBPP	6.13	6.13	6.14
MT-Bench	4.23	4.19	4.19

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}
}