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  5. DFlash
Inference & Deploymentaka block diffusion speculative decoding

DFlash

DFlash is a block-diffusion speculative decoding method where a small drafter proposes fixed-size token blocks that a target model verifies in parallel.

Ask Melo about this← all terms

From Chen et al. (arXiv 2602.06036), DFlash trains a lightweight drafter to predict blocks of tokens—typically 16 at a time—while the full target model verifies the block in one forward pass and accepts a greedy prefix. Implementations such as dflash-mlx on Apple Silicon and Meta's Muse Glimmer drafter use it to raise decode throughput without changing greedy output quality when verification is lossless.

Related terms

Speculative DecodingInference EngineContinuous BatchingLoad SheddingPipeline ParallelismRollback

Where DFlash comes up

  • DFlash-MLX Brings Lossless Speculative Decoding to Apple Silicon — Up to ~189 tok/s on M5 Max
  • Meta Muse Glimmer: A 30B Open-Weight Agentic Model for Local AI
  • DeepSeek DSpark: speculative decoding for V4 Flash and Pro (51–400% faster inference guide 2026)
  • Lily: Perplexity's Custom Inference Engine for Apple Silicon