Flint-DFlash2

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This repository contains the DFlash 2 draft model for RecursiveSI/flint. It is not a standalone language model: it runs inside a speculative decoding server and drafts tokens for the target model to verify.

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. Flint support is in an open pull request (#45624), so install it at the commit we validated:

git clone https://github.com/sgl-project/sglang.git
cd sglang
git fetch origin 37bc771d2b253e2d87d27ace71297154874ee3b4
git checkout 37bc771d2b253e2d87d27ace71297154874ee3b4
pip install -e "python[all]"

sglang serve RecursiveSI/flint \
  --enable-fp32-lm-head \
  --reasoning-parser flint \
  --tool-call-parser flint \
  --speculative-algorithm DFLASH \
  --speculative-draft-model-path incoai/Flint-DFlash2

See the blog post for more details.

Evaluation

• Runtime: SGLang (#45624 at 37bc771d) on one NVIDIA GB300 (TP1), in BF16, with FlashAttention 4 for target and draft attention
• Speculation block size: 8 (7 draft tokens per verification step)
• Sampling: Flint's default parameters (temperature 1.0, top-p 0.95, top-k 20), with its default chat template and reasoning
• Maximum new tokens: 4096
• Samples: 128 at concurrency 1; 1,024 at concurrency 8 and 32
• Prompts: benchmark formatting and repeated sampling from z-lab/dflash

We compare autoregressive decoding and DFlash 2, which proposes seven draft tokens per verification step.

Acceptance Length

Acceptance length is the per-request mean of completion tokens divided by verification steps at concurrency 1. Higher is better.

Throughput

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

Concurrency 1

Concurrency 8

Concurrency 32

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}
}