DFlash is an open source block-diffusion speculative decoding method that replaces sequential autoregressive drafting with parallel block-level token prediction. On NVIDIA Blackwell hardware, it delivers up to 15x throughput improvement over autoregressive decoding for gpt-oss-120b at high interactivity targets, and outperforms EAGLE-3 speculative decoding by 1.5x. The technique uses three key mechanisms: block-diffusion drafting, target hidden-state conditioning, and KV injection. Twenty model checkpoints covering Qwen, Llama, Gemma, Kimi K2.6, and gpt-oss families are available on Hugging Face, with integration support for TensorRT-LLM, vLLM (via the Speculators library), and SGLang requiring minimal config changes and no application refactoring.
