# Qwen3.8 27B DFlash2

URL: https://interfaze.ai/models/z-labqwen38-27b-dflash2

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Qwen3.8 27B DFlash2 by z-lab, a text-generation model with multimodal capabilities. Understand and compare multimodal features, benchmarks, and capabilities.

## Comparison

| Feature | Qwen3.8 27B DFlash2 | Interfaze |
| --- | --- | --- |
| Input Modalities | text, image, video | image, text, audio, video, document |
| Native OCR | No | Yes |
| Long Document Processing | No | Yes |
| Language Support | unknown | 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 | Qwen3.8 27B DFlash2 | Interfaze |
| --- | --- | --- |
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |

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View model card on [Hugging Face](https://huggingface.co/z-lab/Qwen3.8-27B-DFlash2)

[Blog](https://inco.ai/blog/dflash2/) | [GitHub](https://github.com/z-lab/dflash)

This repository contains the DFlash 2 draft model for [`Qwen/Qwen3.8-27B`](https://huggingface.co/Qwen/Qwen3.8-27B). It is not a standalone language model: it runs inside a speculative decoding server and drafts tokens for the target model to verify. This repository is a mirror of [`incoai/Qwen3.8-27B-DFlash2`](https://huggingface.co/incoai/Qwen3.8-27B-DFlash2).

DFlash 2 is a block-diffusion drafter for speculative decoding. It predicts a whole block of tokens in a single pass and keeps the top candidates at every position. A lightweight selector then traces one coherent path through them. Two-tap dynamic convolutions in the backbone keep the draft from decaying toward the end of the block. Decoding is lossless: greedy output matches the target model exactly, and sampling preserves its distribution.

## Quick Start

Serve with [SGLang](https://github.com/sgl-project/sglang):

```
pip install "sglang[all] @ git+https://github.com/sgl-project/sglang.git#subdirectory=python"

python -m sglang.launch_server \
  --model-path Qwen/Qwen3.8-27B \
  --speculative-algorithm DFLASH \
  --speculative-draft-model-path incoai/Qwen3.8-27B-DFlash2 \
  --speculative-num-draft-tokens 8
```

Or with [vLLM](https://github.com/vllm-project/vllm):

```
pip install -U "vllm @ git+https://github.com/vllm-project/vllm.git@refs/pull/52816/head"

vllm serve Qwen/Qwen3.8-27B \
  --speculative-config '{
    "method": "dflash",
    "model": "incoai/Qwen3.8-27B-DFlash2",
    "num_speculative_tokens": 7
  }'
```

See the [blog post](https://inco.ai/blog/dflash2/) for other engines and more details.

## Evaluation

-   Runtime: SGLang on one NVIDIA H200, with FlashAttention 3 for target and draft attention
-   Speculation block size: 8 (7 draft tokens per verification step)
-   Sampling: Qwen3.8's officially recommended parameters (temperature 1.0, top-p 0.95, top-k 20), with `xhigh` reasoning effort
-   Maximum new tokens: 4096
-   Prompts: benchmark formatting from [`z-lab/dflash`](https://github.com/z-lab/dflash)

We compare autoregressive decoding, Qwen3.8's built-in seven-token MTP, a community DSpark drafter ([`RadixArk/Qwen3.8-27B-DSpark`](https://huggingface.co/RadixArk/Qwen3.8-27B-DSpark)), and DFlash 2. All speculative methods propose seven draft tokens per verification step.

### Acceptance Length

Acceptance length is the per-request mean of completion tokens divided by verification steps. Higher is better.

| Task | MTP | DSpark | DFlash 2 |
| --- | --- | --- | --- |
| GSM8K | 5.02 | 4.36 | 5.46 |
| MATH-500 | 4.72 | 3.92 | 5.28 |
| HumanEval | 3.91 | 3.30 | 4.39 |
| MBPP | 3.99 | 3.51 | 4.79 |
| MT-Bench | 3.74 | 3.01 | 4.10 |

### Throughput

Throughput is total output tokens divided by end-to-end wall time. Each cell shows `output tok/s (speedup vs. autoregressive)`.

#### Concurrency 1

| Task | Autoregressive | MTP | DSpark | DFlash 2 |
| --- | --- | --- | --- | --- |
| GSM8K | 68.9 | 178.5 (2.59×) | 185.3 (2.69×) | 236.1 (3.43×) |
| MATH-500 | 69.0 | 172.8 (2.51×) | 174.5 (2.53×) | 230.7 (3.34×) |
| HumanEval | 69.0 | 151.9 (2.20×) | 159.9 (2.32×) | 214.6 (3.11×) |
| MBPP | 69.0 | 153.1 (2.22×) | 163.3 (2.37×) | 226.9 (3.29×) |
| MT-Bench | 68.9 | 134.9 (1.96×) | 137.6 (2.00×) | 184.0 (2.67×) |

#### Concurrency 8

| Task | Autoregressive | MTP | DSpark | DFlash 2 |
| --- | --- | --- | --- | --- |
| GSM8K | 467.2 | 1,022.1 (2.19×) | 1,040.8 (2.23×) | 1,328.7 (2.84×) |
| MATH-500 | 480.0 | 1,023.5 (2.13×) | 1,025.8 (2.14×) | 1,368.3 (2.85×) |
| HumanEval | 483.4 | 934.2 (1.93×) | 956.5 (1.98×) | 1,291.5 (2.67×) |
| MBPP | 478.0 | 938.1 (1.96×) | 974.1 (2.04×) | 1,328.0 (2.78×) |
| MT-Bench | 480.5 | 835.2 (1.74×) | 802.3 (1.67×) | 1,090.2 (2.27×) |

#### Concurrency 32

| Task | Autoregressive | MTP | DSpark | DFlash 2 |
| --- | --- | --- | --- | --- |
| GSM8K | 1,329.8 | 1,381.1 (1.04×) | 1,506.5 (1.13×) | 1,922.5 (1.45×) |
| MATH-500 | 1,505.8 | 1,415.6 (0.94×) | 1,429.0 (0.95×) | 1,951.8 (1.30×) |
| HumanEval | 1,546.5 | 1,296.8 (0.84×) | 1,330.1 (0.86×) | 1,799.0 (1.16×) |
| MBPP | 1,507.7 | 1,314.9 (0.87×) | 1,361.3 (0.90×) | 1,886.8 (1.25×) |
| MT-Bench | 1,507.4 | 1,159.7 (0.77×) | 1,115.5 (0.74×) | 1,525.3 (1.01×) |

## Citation

If you find DFlash 2 useful, please cite:

```
@misc{inco2026dflash2,
  title  = {{DFlash 2: Keep Drafting Parallel}},
  author = {{Inco AI}},
  year   = {2026},
  month  = {August},
  url    = {https://inco.ai/blog/dflash2/}
}
```

Please also cite the original DFlash paper:

```
@inproceedings{chen2026dflash,
  title     = {{DFlash: Block Diffusion for Flash Speculative Decoding}},
  author    = {Chen, Jian and Liang, Yesheng and Liu, Zhijian},
  booktitle = {International Conference on Machine Learning (ICML)},
  year      = {2026}
}
```

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