Qwen3.8-27B-DFlash2
This repository contains the DFlash 2 draft model for
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.
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:
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:
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 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
xhighreasoning effort - Maximum new tokens: 4096
- Prompts: benchmark formatting from
z-lab/dflash
We compare autoregressive decoding, Qwen3.8's built-in seven-token MTP,
a community DSpark drafter
(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}
}