# Gemma 4 31B It DFlash

URL: https://interfaze.ai/models/z-labgemma-4-31b-it-dflash

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Gemma 4 31B It DFlash by z-lab, a text-generation model with multimodal capabilities. Understand and compare multimodal features, benchmarks, and capabilities.

## Comparison

| Feature | Gemma 4 31B It DFlash | Interfaze |
| --- | --- | --- |
| Input Modalities | text, image, video | image, text, audio, video, document |
| Native OCR | No | Yes |
| Long Document Processing | No | Yes |
| Language Support | 140 partial | 162+ |
| Native Speech-to-Text | No | Yes |
| Native Object Detection | No | Yes |
| Guardrail Controls | Yes | Yes |
| Context Input Size | 256K | 1M |
| Tool Calling | Yes | Tool calling supported + built in browser, code execution and web search |

### Scaling

| Feature | Gemma 4 31B It DFlash | Interfaze |
| --- | --- | --- |
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)

View model card on [Hugging Face](https://huggingface.co/z-lab/gemma-4-31B-it-DFlash)

[**Paper**](https://arxiv.org/abs/2602.06036) | [**GitHub**](https://github.com/z-lab/dflash) | [**Blog**](https://z-lab.ai/projects/dflash/)

**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](https://huggingface.co/google/gemma-4-31B-it).

## Quick Start

### Installation

vLLM: until Gemma4 DFlash support is merged, install vLLM from [PR #41703](https://github.com/vllm-project/vllm/pull/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:

```
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](https://davidwa.ng/) for his outstanding engineering support on this project. We are also grateful to [Modal](https://modal.com/), [InnoMatrix](https://innomatrix.ai), and [Yotta Labs](https://www.yottalabs.ai/) 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](https://forms.gle/4YNwfqb4nJdqn6hq9).

```
@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}
}
```

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