arXiv — Machine Learning · · 3 min read

Exact Memory-Time Optimization for Prefix-Cached Language Model Serving

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Computer Science > Machine Learning

arXiv:2610.02766 (cs)
[Submitted on 2 Oct 2026]

Title:Exact Memory-Time Optimization for Prefix-Cached Language Model Serving

Authors:Shivam Gupta
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Abstract:Retaining language-model prefix states trades recomputation against storage time. Optimizing each cached block independently can overcount savings: a resident block is usable only when the required preceding prefix is also available. We introduce Prefix-Certificate Retention (PCR), an exact finite-trace formulation for static, grouped, reset-on-access timeouts. Usable-prefix rewards become nodes whose prerequisites are timeout thresholds and preceding hit certificates. The resulting maximum-weight closure reduces to one minimum cut, with graph size linear in the number of block lookups and timeout choices. A breakpoint theorem extends the construction to all nonnegative timeouts without discretization error. We also derive a linear-time-in-grid-size dynamic program for ordered timeouts and bounds that certify the cost of this restriction. Exhaustive small-instance checks and chronological replay of 39,632 public Mooncake requests validate the formulation. On the fixed grid, ordered timeouts attain the unrestricted training optimum in 118 of 120 trace-grouping-price cases. Heterogeneous retention improves several held-out memory-time tradeoffs, but finer training optimization does not uniformly improve transfer. The contribution is a tractable optimization model and an auditable benchmark for retention policies; the experiments measure usable prefix blocks and storage time, not GPU latency.
Comments: 16 pages, 5 figures. Code and reproducibility artifacts: this https URL
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2610.02766 [cs.LG]
  (or arXiv:2610.02766v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2610.02766
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Shivam Gupta [view email]
[v1] Fri, 2 Oct 2026 03:47:10 UTC (121 KB)
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