When Fancy Eviction Fails: Rethinking Cache Replacement For LLM Prefix Reuse

Abstract:Long-running LLM applications repeatedly send growing context, making prefix caching critical for reducing prefill cost. Yet prefix-cache behavior under agentic workloads remains poorly understood. We study production traces from two companies and evaluate 14 eviction algorithms across HBM-constrained and large memory-pool settings. Despite a large gap to Belady, sophisticated policies designed for traditional caches provide little benefit over LRU. The reason is structural: prefix reuse is dominated by the regular pacing of active sessions, making recency unusually predictive. Prefix caching nevertheless introduces new challenges, including heavy-tailed session footprints and highly variable miss costs as attention computation grows with sequence length. We introduce the compute-savings ratio and two offline oracles to quantify these effects. Our results show that effective prefix-cache management should retain recency as its foundation while selectively adding quick demotion for one-hit prefixes, compute-aware partial eviction for expensive misses, and capacity-dependent eviction granularity. We will release the traces and simulator to support future research.

Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)

arXiv:2609.28870 [cs.DC]

(or arXiv:2609.28870v1 [cs.DC] for this version)

https://doi.org/10.48550/arXiv.2609.28870 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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