Why LLM Inference Teams Should Treat KV Cache as a Scheduling Problem

Most AI inference servers manage KV cache using simple LRU eviction policies, treating all cached blocks as equivalent regardless of how they were generated. However, different workload types — such as agentic loops, multi-turn chat, and single-shot RAG queries — have vastly different probabilities of future reuse, making their KV blocks unequal in value. Evicting high-value agent state to free space for low-reuse RAG blocks forces costly prefill recomputations that compete for GPU resources and spike latency across other requests. The article argues that KV cache eviction is fundamentally an admission-control and scheduling problem, not a memory-management one. Teams that ignore workload-aware eviction strategies are leaving significant throughput and latency gains unrealized, regardless of how well they tune batch size or quantization.