Sparse-dLLM: Accelerating Diffusion LLMs with Dynamic Cache Eviction
Authors: name: Yuerong Song, name: Xiaoran Liu, name: Ruixiao Li, name: Zhigeng Liu, name: Zengfeng Huang, name: Qipeng Guo, name: Ziwei He, name: Xipeng Qiu

Diffusion Large Language Models (dLLMs) enable breakthroughs in reasoning and
parallel decoding but suffer from prohibitive quadratic computational
complexity and memory overhead during inference. Current caching techniques
accelerate decoding by storing full-layer states, yet impose substantial memory
usage that limit long-context applications. Our analysis of attention patterns
in dLLMs reveals persistent cross-layer sparsity, with pivotal tokens remaining
salient across decoding steps and low-relevance tokens staying unimportant,
motivating selective cache eviction. We propose Sparse-dLLM, the first
training-free framework integrating dynamic cache eviction with sparse
attention via delayed bidirectional sparse caching. By leveraging the stability
of token saliency over steps, it retains critical tokens and dynamically evicts
unimportant prefix/suffix entries using an attention-guided strategy. Extensive
experiments on LLaDA and Dream series demonstrate Sparse-dLLM achieves up to
10times higher throughput than vanilla dLLMs, with comparable performance
and similar peak memory costs, outperforming previous methods in efficiency and
effectiveness.