Towards Efficient Large Language Model Serving: A Survey on System-Aware KV Cache Optimization
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Computer Science > Machine Learning
Title:Towards Efficient Large Language Model Serving: A Survey on System-Aware KV Cache Optimization
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.
| Comments: | Accepted to ACL 2026 as a Findings paper |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2607.08057 [cs.LG] |
| (or arXiv:2607.08057v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.08057
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | Findings of the Association for Computational Linguistics: ACL 2026 (pp. 38450-38476) |
| Related DOI: | https://doi.org/10.18653/v1/2026.findings-acl.1916
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