# Qwen3.6 35B A3B DFlash

URL: https://interfaze.ai/models/z-labqwen36-35b-a3b-dflash

Qwen3.6 35B A3B DFlash by z-lab, a text-generation model with multimodal capabilities. Understand and compare multimodal features, benchmarks, and capabilities.

## Comparison

| Feature | Qwen3.6 35B A3B DFlash | Interfaze |
| --- | --- | --- |
| Input Modalities | text, image, video | image, text, audio, video, document |
| Native OCR | No | Yes |
| Long Document Processing | No | Yes |
| Language Support | 201 partial | 162+ |
| Native Speech-to-Text | No | Yes |
| Native Object Detection | No | Yes |
| Guardrail Controls | No | Yes |
| Context Input Size | 262.1K | 1M |
| Tool Calling | Yes | Tool calling supported + built in browser, code execution and web search |

### Scaling

| Feature | Qwen3.6 35B A3B 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/Qwen3.6-35B-A3B-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 [Qwen/Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B).

## Quick Start

### Installation

vLLM:

```
uv pip install vllm
uv pip install -U vllm --torch-backend=auto --extra-index-url https://wheels.vllm.ai/nightly
```

SGLang:

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

### Launch Server

vLLM:

```
vllm serve Qwen/Qwen3.6-35B-A3B \
  --speculative-config '{"method": "dflash", "model": "z-lab/Qwen3.6-35B-A3B-DFlash", "num_speculative_tokens": 15}' \
  --attention-backend flash_attn \
  --max-num-batched-tokens 32768
```

SGLang:

```
python -m sglang.launch_server \
    --model-path Qwen/Qwen3.6-35B-A3B \
    --speculative-algorithm DFLASH \
    --speculative-draft-model-path z-lab/Qwen3.6-35B-A3B-DFlash \
    --speculative-num-draft-tokens 16 \
    --tp-size 1 \
    --attention-backend fa3 \
    --mem-fraction-static 0.75 \
    --mamba-scheduler-strategy extra_buffer \
    --trust-remote-code
```

> **Tip:** For long-context or agentic workloads, add `--speculative-dflash-draft-window-size WINDOW_SIZE` to enable sliding-window attention for the drafter.

### Usage

```
from openai import OpenAI

client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")

response = client.chat.completions.create(
    model="Qwen/Qwen3.6-35B-A3B",
    messages=[{"role": "user", "content": "Write a quicksort in Python."}],
    max_tokens=4096,
    temperature=0.0
)
print(response.choices[0].message.content)
```

## Benchmark Results

**Setup:** Single NVIDIA B200, SGLang, thinking enabled, max output length 4096. We report end-to-end throughput, including prefill time. See our [GitHub repository](https://github.com/z-lab/dflash) for reproduction scripts.

### Throughput and Speedup

DFlash achieves up to **2.9x** speedup at concurrency 1.

_Tokens/sec (speedup vs. autoregressive baseline)_

**Block Size = 16**

| Task | Concurrency | AR | DFlash |
| --- | --- | --- | --- |
| Math500 | 1 | 234 | 682 (2.9x) |
|  | 8 | 1266 | 3138 (2.5x) |
|  | 16 | 1954 | 4813 (2.5x) |
|  | 32 | 2755 | 6520 (2.4x) |
| GSM8K | 1 | 235 | 556 (2.4x) |
|  | 8 | 1236 | 2564 (2.1x) |
|  | 16 | 1886 | 3821 (2.0x) |
|  | 32 | 2699 | 5239 (1.9x) |
| HumanEval | 1 | 238 | 603 (2.5x) |
|  | 8 | 1255 | 2800 (2.2x) |
|  | 16 | 1944 | 4208 (2.2x) |
|  | 32 | 2767 | 5782 (2.1x) |
| MBPP | 1 | 235 | 559 (2.4x) |
|  | 8 | 1224 | 2538 (2.1x) |
|  | 16 | 1948 | 3816 (2.0x) |
|  | 32 | 2780 | 5378 (1.9x) |
| MT-Bench | 1 | 233 | 442 (1.9x) |
|  | 8 | 1238 | 2028 (1.6x) |
|  | 16 | 1885 | 2997 (1.6x) |
|  | 32 | 2633 | 4034 (1.5x) |
| Alpaca | 1 | 235 | 393 (1.7x) |
|  | 8 | 1221 | 1782 (1.5x) |
|  | 16 | 1844 | 2567 (1.4x) |
|  | 32 | 2579 | 3689 (1.4x) |

**Block Size = 8**

| Task | Concurrency | AR | DFlash |
| --- | --- | --- | --- |
| Math500 | 1 | 234 | 617 (2.6x) |
|  | 8 | 1266 | 2839 (2.2x) |
|  | 16 | 1954 | 4465 (2.3x) |
|  | 32 | 2755 | 6614 (2.4x) |
| GSM8K | 1 | 235 | 540 (2.3x) |
|  | 8 | 1236 | 2466 (2.0x) |
|  | 16 | 1886 | 3899 (2.1x) |
|  | 32 | 2699 | 5713 (2.1x) |
| HumanEval | 1 | 238 | 561 (2.4x) |
|  | 8 | 1255 | 2655 (2.1x) |
|  | 16 | 1944 | 4135 (2.1x) |
|  | 32 | 2767 | 6059 (2.2x) |
| MBPP | 1 | 235 | 497 (2.1x) |
|  | 8 | 1224 | 2324 (1.9x) |
|  | 16 | 1948 | 3636 (1.9x) |
|  | 32 | 2780 | 4884 (1.8x) |
| MT-Bench | 1 | 233 | 438 (1.9x) |
|  | 8 | 1238 | 2060 (1.7x) |
|  | 16 | 1885 | 3182 (1.7x) |
|  | 32 | 2633 | 4720 (1.8x) |
| Alpaca | 1 | 235 | 407 (1.7x) |
|  | 8 | 1221 | 1880 (1.5x) |
|  | 16 | 1844 | 2903 (1.6x) |
|  | 32 | 2579 | 4115 (1.6x) |

### Acceptance Length

| Task | B8 | B16 |
| --- | --- | --- |
| Math500 | 5.56 | 7.35 |
| GSM8K | 5.21 | 6.73 |
| HumanEval | 5.09 | 6.44 |
| MBPP | 4.78 | 5.83 |
| MT-Bench | 4.20 | 5.14 |
| Alpaca | 3.94 | 4.62 |

## 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}
}
```

## Want more deterministic results?

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