Flash-dLLM introduces IO-aware KV caching and parallel decoding to speed up inference and cut memory use in diffusion large language models (dLLMs), addressing the lack of effective Key-Value caching that has limited practical deployment of these non-autoregressive alternatives to autoregressive LLMs. The work targets the inefficient inference that currently constrains dLLM deployment.
Diffusion Large Language Models (dLLMs) have recently emerged as a promising alternative to autoregressive LLMs by enabling non-autoregressive text generation. However, their practical deployment remains limited by inefficient inference, largely due to the absence of effective Key-Value (KV) caching
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