Affix Cache for Diffusion Large Language Models

Abstract:Diffusion Large Language Models (DLLMs) enable non-autoregressive decoding and bidirectional context modeling, but efficient inference remains challenging. Unlike autoregressive systems, whose key-value (KV) cache can be reused for shared prefixes, DLLMs couple the KV states of shared context tokens with evolving generated tokens through bidirectional attention, making naive cache reuse stale while full recomputation is expensive. We present ACache, an affix-oriented cache reuse mechanism for shared text spans in DLLMs beyond prefixes. ACache identifies a small request-specific subset of critical affix tokens, called Anchor Tokens, by measuring their influence on masked generation tokens, and selectively recomputes the KV states of only these tokens while reusing the remaining affix cache. Built on Fast-dLLM, ACache recovers the accuracy loss caused by direct affix-cache reuse across different settings when recomputing around 20% of affix tokens. We also build a shared-prefix prototype on top of the Nano-vLLM engine, showing that ACache reduces recompute latency by up to 55.7% and improves end-to-end throughput by up to 1.68$\times$.

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2608.26140 [cs.CL]

(or arXiv:2608.26140v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.26140 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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