Abstract
Despite the rapid advancements of large language models (LLMs), LLM serving systems remain memory-intensive and costly. The key-value (KV) cache, which stores KV tensors during autoregressive decoding, is crucial for enabling low-latency, high-throughput LLM inference serving. This survey focuses 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.
Blogger's Review: This paper offers a fresh perspective on KV cache analysis from a system behavior standpoint, facilitating the development of efficient serving for large language models. It points out future optimization directions and encourages deeper discussions on practical implementations of these theories.