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    <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. 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.</abstract>
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%0 Conference Proceedings
%T Towards Efficient Large Language Model Serving: A Survey on System-Aware KV Cache Optimization
%A Jiang, Jiantong
%A Yang, Peiyu
%A Zhang, Rui
%A Liu, Feng
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F jiang-etal-2026-towards
%X 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. 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.
%U https://aclanthology.org/2026.findings-acl.1916/
%P 38450-38476

Markdown (Informal)

[Towards Efficient Large Language Model Serving: A Survey on System-Aware KV Cache Optimization](https://aclanthology.org/2026.findings-acl.1916/) (Jiang et al., Findings 2026)

• Towards Efficient Large Language Model Serving: A Survey on System-Aware KV Cache Optimization (Jiang et al., Findings 2026)

ACL

• Jiantong Jiang, Peiyu Yang, Rui Zhang, and Feng Liu. 2026. Towards Efficient Large Language Model Serving: A Survey on System-Aware KV Cache Optimization. In Findings of the Association for Computational Linguistics: ACL 2026, pages 38450–38476, San Diego, California, United States. Association for Computational Linguistics.