Not All Tokens Are Worth Caching: Learning Semantic-Aware Eviction for LLM Prefix Caches

TL;DR

SAECache introduces a semantic-aware, adaptive eviction policy for LLM prefix caches, significantly improving reuse efficiency by learning token importance and workload characteristics online.

Contribution

It proposes a novel multi-queue, semantic-aware eviction policy with online learning, outperforming existing methods in LLM prefix cache management.

Findings

SAECache achieves 1.4x-2.7x TTFT improvement over baselines.

Fixed-parameter policies can degrade by up to 2.7x under workload mismatch.

Adaptive learning eliminates manual tuning and adapts to workload variations.

Abstract

Prefix caching is a key optimization in Large Language Model (LLM) serving, reusing attention Key-Value (KV) states across requests with shared prompt prefixes to reduce expensive prefill computation. However, its benefit depends critically on the eviction policy as GPU memory is scarce, and existing policies such as LRU largely treat cached blocks uniformly. This view ignores a fundamental property of LLM prompts: not all tokens are equally worth caching. We show that different token types within a prompt, including system prompts, user queries, tool outputs, model responses, and chain-of-thought reasoning, exhibit up to 756x variation in reuse rates, yet no existing eviction policy exploits this signal. In this paper, we present SAECache (Semantic-Adaptive Eviction for prefix caches), a semantic-adaptive prefix cache eviction policy that addresses this gap through three innovations: (1) a multi-queue architecture that routes KV blocks to task-specific queues with tailored priority metrics, capturing both session reuse in multi-turn requests and structural reuse in templated single-turn requests; (2) a semantic-aware token weighting mechanism that learns the reuse value of different token types online through eviction feedback; and (3) a fully adaptive online learning schema for all parameter updates, including log-normal timing parameters, position decay power, queue weights, and meta-parameters, which eliminates manual tuning and enables automatic adaptation to deployment-specific workload characteristics. Through extensive evaluation across heterogeneous workloads, we demonstrate that SAECache achieves 1.4x-2.7x TTFT improvement over production-style baselines, while fixed-parameter alternatives can degrade by up to 2.7x under workload mismatch -- a failure mode our adaptive approach avoids entirely.