Diffusion LLMs can be dramatically accelerated by jointly optimizing memory I/O patterns in KV caching and using the model itself for draft-and-verify decoding, rather than treating these optimizations separately.
This paper presents Flash-dLLM, a system that speeds up diffusion language models (an alternative to traditional autoregressive LLMs) by optimizing how the model stores and reuses computation results during inference.