---

base_model:

• z-lab/Qwen3.5-4B-DFlash

license: apache-2.0

inference: false

tags:

- dflash

- speculative-decoding

- speculative-decoding-draft

- block-diffusion

- draft-model

- diffusion-language-model

- efficiency

- qwen

- qwen3

- qwen3.5

- llama.cpp

---

Quantized version of z-lab/Qwen3.5-4B-DFlash.

Works with mainline llama.cpp, will NOT work with forks.

Their model card is pasted as is below.

Files:

• model.safetensors - original unquantized safetensors
• model.gguf - unquantized bf16 GGUF
• Qwen3.5-4B-DFlash.gguf - GGUF quantized to Q5_K_M

---

Qwen3.5-4B-DFlash

Paper | Github | Blog

This DFlash draft model is a joint retrain from Z-Lab and Modal, trained with 40k sequence length and sliding-window attention for improved long-context performance. It is mirrored across the following Hugging Face repositories:

• z-lab/Qwen3.5-4B-DFlash
• modal-labs/Qwen3.5-4B-DFlash

This repository contains a DFlash draft model for Qwen/Qwen3.5-4B. It is not a standalone language model. It is intended to be paired with the target model in a speculative decoding server.

DFlash uses a lightweight block diffusion draft model to propose multiple tokens in parallel. The target model verifies those proposals, improving serving throughput while preserving the target model's output distribution.

<div align="center">

<img src="assets/dflash_system.png" alt="DFlash Architecture" width="85%">

</div>

Quick Start

Installation

SGLang

Install a recent SGLang build with DFlash support:

uv pip install --upgrade "sglang[all]"

For best performance on Blackwell GPUs, use an SGLang build that includes DFlash, FA4/TRT-LLM attention, and FlashInfer support.

vLLM

For vLLM support, please refer to vllm-project/vllm#40898. We will update the PR to make it merge-ready soon.

Launch Server

This model should be used with an inference server that supports DFlash speculative decoding. An example SGLang deployment is:

export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1

python -m sglang.launch_server \
  --model-path Qwen/Qwen3.5-4B \
  --trust-remote-code \
  --speculative-algorithm DFLASH \
  --speculative-draft-model-path z-lab/Qwen3.5-4B-DFlash \
  --speculative-dflash-block-size 8 \
  --speculative-draft-attention-backend fa4 \
  --attention-backend trtllm_mha \
  --linear-attn-prefill-backend flashinfer \
  --linear-attn-decode-backend flashinfer \
  --mamba-scheduler-strategy extra_buffer \
  --tp-size 1 \
  --max-running-requests 32 \
  --cuda-graph-max-bs-decode 32 \
  --cuda-graph-backend-prefill tc_piecewise \
  --enable-flashinfer-allreduce-fusion \
  --mem-fraction-static 0.8 \
  --host 0.0.0.0 \
  --port 30000

Block size 8 is the recommended default for higher-concurrency serving. Block size 16 gives longer accept lengths and strong concurrency-1 throughput in most workloads.

Benchmark Results

We benchmarked DFlash against the autoregressive baseline and Qwen's built-in MTP draft path. DFlash reaches up to 4.60x speedup at concurrency 1 and 2.61x at concurrency 32. Across the benchmark suite, DFlash delivers higher throughput than MTP at every matched setting where both completed.

Setup

• Runtime: SGLang on 1x NVIDIA B200 GPU, tensor parallel size 1, bfloat16
• Backends: trtllm_mha target attention, fa4 DFlash draft attention, flashinfer linear-attention prefill and decode
• Workloads: GSM8K, MATH500, HumanEval, MBPP, and MT-Bench with the Qwen chat template
• Decoding: greedy, thinking enabled, max output length 4096 tokens
• Measurement: 5 independent runs per configuration at concurrency 1 and 32 with continuous batching
• Throughput: generated output tokens / wall-clock benchmark time, including prefill and scheduling
• Accept length: completion_tokens / spec_verify_ct per generation turn, averaged across generation turns

Throughput and Speedup

Each cell is output tok/s (speedup). Bold marks the fastest speculative configuration in each row.

Concurrency 1

| Workload | Baseline | MTP steps=3 | DFlash block=4 | MTP steps=7 | DFlash block=8 | MTP steps=15 | DFlash block=16 |

| --- | --- | --- | --- | --- | --- | --- | --- |

| gsm8k | 356.0 (1.00x) | 739.3 (2.08x) | 859.6 (2.41x) | 772.0 (2.17x) | 1226.8 (3.45x) | 585.3 (1.64x) | 1387.4 (3.90x) |

| math500 | 360.2 (1.00x) | 763.7 (2.12x) | 899.4 (2.50x) | 832.5 (2.31x) | 1355.7 (3.76x) | 645.1 (1.79x) | 1636.5 (4.54x) |

| humaneval | 355.8 (1.00x) | 739.7 (2.08x) | 892.2 (2.51x) | 803.0 (2.26x) | 1325.2 (3.72x) | 594.7 (1.67x) | 1634.9 (4.60x) |

| mbpp | 360.2 (1.00x) | 723.9 (2.01x) | 895.6 (2.49x) | 737.2 (2.05x) | 1314.9 (3.65x) | 557.4 (1.55x) | 1494.9 (4.15x) |

| mt-bench | 356.5 (1.00x) | 708.8 (1.99x) | 806.7 (2.26x) | 699.0 (1.96x) | 1085.3 (3.04x) | 528.7 (1.48x) | 1211.0 (3.40x) |

Concurrency 32

| Workload | Baseline | MTP steps=3 | DFlash block=4 | MTP steps=7 | DFlash block=8 | MTP steps=15 | DFlash block=16 |

| --- | --- | --- | --- | --- | --- | --- | --- |

| gsm8k | 7501.6 (1.00x) | 12716.8 (1.70x) | 15015.4 (2.00x) | 12419.5 (1.66x) | 17613.7 (2.35x) | 8696.7 (1.16x) | 14203.5 (1.89x) |

| math500 | 7573.5 (1.00x) | 13482.4 (1.78x) | 15876.2 (2.10x) | 13636.5 (1.80x) | 19759.4 (2.61x) | 9663.1 (1.28x) | 17060.5 (2.25x) |

| humaneval | 7286.1 (1.00x) | 12313.2 (1.69x) | 15284.3 (2.10x) | 12326.0 (1.69x) | 18792.5 (2.58x) | 9115.4 (1.25x) | 16492.0 (2.26x) |

| mbpp | 7065.9 (1.00x) | 11032.0 (1.56x) | 14641.6 (2.07x) | 10842.3 (1.53x) | 17908.0 (2.53x) | 7744.1 (1.10x) | 15427.4 (2.18x) |

| mt-bench | 6797.1 (1.00x) | 11514.5 (1.69x) | 12715.5 (1.87x) | 11155.8 (1.64x) | 14623.7 (2.15x) | 8045.7 (1.18x) | 12007.6 (1.77x) |

Accept Length

Mean accept length at concurrency 1. Bold marks the higher value in each matched MTP/DFlash pair.

| Workload | MTP steps=3 | DFlash block=4 | MTP steps=7 | DFlash block=8 | MTP steps=15 | DFlash block=16 |

| --- | --- | --- | --- | --- | --- | --- |

| gsm8k | 3.422 | 3.427 | 5.133 | 5.299 | 6.175 | 6.748 |

| math500 | 3.502 | 3.528 | 5.345 | 5.650 | 6.468 | 7.478 |

| humaneval | 3.448 | 3.551 | 5.193 | 5.684 | 6.147 | 7.719 |

| mbpp | 3.272 | 3.418 | 4.611 | 5.236 | 5.326 | 6.527 |

| mt-bench | 3.266 | 3.234 | 4.626 | 4.704 | 5.610 | 5.933 |

Citation

If you find DFlash useful, please cite the original paper:

@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}
}