Qwen3.6-35B-A3B-DFlash EXL3 6.00bpw HB8

This repository contains an EXL3 quantization of z-lab/Qwen3.6-35B-A3B-DFlash for ExLlamaV3.

This is a DFlash draft model for Qwen/Qwen3.6-35B-A3B. It is not a standalone chat or completion model. It is intended to be paired with the target model in a speculative decoding server that supports DFlash draft models and EXL3 / ExLlamaV3 quantized weights.

Quantization

• Source model: z-lab/Qwen3.6-35B-A3B-DFlash
• Target model: Qwen/Qwen3.6-35B-A3B
• Quantizer: exllamav3-1.4.0
• Format: exl3
• Bits per weight: 6.00
• Head bits: 8
• Calibration: 250 rows, 2048 columns
• Output scales: always
• Codebook: mul1
• DFlash block size in config: 16
• Files: 1 .safetensors file

Compatibility

This is not the original BF16 Transformers checkpoint. Use it only with loaders or serving stacks that explicitly support EXL3 / ExLlamaV3 weights for this DFlash draft architecture.

If your serving stack expects the standard DFlash repository layout or BF16 weights, use the upstream model instead:

• z-lab/Qwen3.6-35B-A3B-DFlash
• modal-labs/Qwen3.6-35B-A3B-DFlash

Notes

No additional fine-tuning was performed. Capabilities, limitations, license terms, and safety considerations from the upstream DFlash and target Qwen model still apply, with the usual caveat that quantization can change quality and numerical behavior.

The original upstream model card is included below for attribution and DFlash details.

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Original Model Card: Qwen3.6-35B-A3B-DFlash

Qwen3.6-35B-A3B-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/Qwen3.6-35B-A3B-DFlash
• modal-labs/Qwen3.6-35B-A3B-DFlash

This repository contains a DFlash draft model for Qwen/Qwen3.6-35B-A3B. 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.

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/Qwen3.6-35B-A3B \
  --trust-remote-code \
  --speculative-algorithm DFLASH \
  --speculative-draft-model-path z-lab/Qwen3.6-35B-A3B-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 3.61x speedup at concurrency 1 and 2.89x 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

Concurrency 32

Accept Length

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

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}
}