DFlash Speculative Decoding Drafts Whole Token Blocks in Parallel for Up to 15x Higher Throughput on NVIDIA Blackwell

Summary

<p>UC San Diego's DFlash replaces autoregressive drafting with a lightweight block diffusion model for speculative decoding. It drafts whole token blocks in a single forward pass and conditions on target hidden features through KV injection. The paper reports up to 6.08x lossless speedup on Qwen3-8B, while NVIDIA reports up to 15x throughput on Blackwell at fixed interactivity. DFlash ships 20 checkpoints and supports SGLang, vLLM, and TensorRT-LLM.</p> <p>The post <a href="https://www.marktechpost.com/2026/06/24/dflash-speculative-decoding-drafts-whole-token-blocks-in-parallel-for-up-to-15x-higher-throughput-on-nvidia-blackwell/">DFlash Speculative Decoding Drafts Whole Token Blocks in Parallel for Up to 15x Higher Throughput on NVIDIA Blackwell</a> appeared first on <a href="https://www.marktechpost.com">MarkTechPost</a>.</p>