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DFlash

Block diffusion draft models for faster LLM speculative decoding

  • AI models and runtimes
View on GitHub
Website

What it is

DFlash is a small block diffusion model that drafts several tokens in parallel, which a larger target LLM then verifies, cutting generation latency. The project publishes draft checkpoints for model families such as Qwen, Gemma, Kimi, MiniMax, GPT-OSS and Llama, plus a Python package with a CLI for generation and benchmarking. It runs locally through Transformers or MLX on Apple Silicon, or against SGLang, vLLM and llama.cpp servers. People who serve open models and want faster decoding use it to pair a draft model with their target and measure the speedup on math and coding datasets.

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Facts

Stars
6,184 GitHub stars.
6,184

Forks
438
Commits
100
Contributors
4
Language
Python
License
MIT
Last commit
Aug 18, 2026
On GitHub since
January 2026
First release
August 2026
Platforms
macOS, Linux, CLI, library
Self-hostable
No
Tags
  • open-weights
  • llm-inference
  • local-ai
  • model-optimization

Last synced from GitHub on Oct 20, 2026

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What people are saying

Updated Oct 21, 2026
  • Bluesky“z-lab / dflash DFlash: Block Diffusion for Flash Speculative Decoding”May 13, 2026 · 0 likes · 0 replies (opens Bluesky in a new tab)
  • Bluesky“I’ve been saying we’ll see more hybrid diffusion transformer models for a while, didn’t expect this to be with the diffusion model being a speculative…”May 8, 2026 · 0 likes · 0 replies (opens Bluesky in a new tab)