Serving Large Language Models (LLMs) at scale requires meeting strict Service Level Objectives (SLOs) under severe computational and memory constraints. Nevertheless, traditional caching strategies fall short: exact-matching and prefix caches neglect query semantics, while state-of-the-art semantic caches remain confined to traditional intuitions, offering little conceptual departure. Building on this, we present SISO, a semantic caching system that redefines efficiency for LLM serving. SISO introduces centroid-based caching to maximize coverage with minimal memory, locality-aware replacement to preserve high-value entries, and dynamic thresholding to balance accuracy and latency under varying workloads. Across diverse datasets, SISO delivers up to 1.71× higher hit ratios and consistently stronger SLO attainment compared to state-of-the-art systems.
@article{arxiv.2508.18736,
title = {Rethinking Caching for LLM Serving Systems: Beyond Traditional Heuristics},
author = {Jungwoo Kim and Minsang Kim and Jaeheon Lee and Chanwoo Moon and Heejin Kim and Taeho Hwang and Woosuk Chung and Yeseong Kim and Sungjin Lee},
journal= {arXiv preprint arXiv:2508.18736},
year = {2025}
}