Towards Efficient Large Language Model Serving: A Survey on System-Aware KV Cache Optimization
Jiantong Jiang, Peiyu Yang, Rui Zhang, Feng Liu · 2026
Despite the rapid advancements of large language models (LLMs), LLM serving systems remain memory-intensive and costly.The keyvalue (KV) cache, which stores KV tensors during autoregressive decoding, is crucial for enabling low-latency, high-throughput LLM inference serving.In this survey, we focus on system-aware KV infrastructure for serving LLMs (abbreviated as sKis).We revisit recent work from a system behavior perspective, organizing existing efforts into three dimensions: execution and scheduling (temporal), placement and migration (spatial), and representation and retention (structural).Furthermore, we analyze cross-behavior co-design affinity and behavior-objective links, highlighting future opportunities.Our work systematizes a rapidly evolving area, providing a foundation for understanding and innovating KV cache designs in modern LLM serving infrastructure.