MemServe: Context Caching for Disaggregated LLM Serving with Elastic
  Memory Pool
Authors: name: Cunchen Hu, name: Heyang Huang, name: Junhao Hu, name: Jiang Xu, name: Xusheng Chen, name: Tao Xie, name: Chenxi Wang, name: Sa Wang, name: Yungang Bao, name: Ninghui Sun, name: Yizhou Shan

Large language model (LLM) serving has transformed from stateless to stateful
systems, utilizing techniques like context caching and disaggregated inference.
These optimizations extend the lifespan and domain of the KV cache,
necessitating a new architectural approach. We present MemServe, a unified
system that integrates both inter-request and intra-request optimizations.
MemServe introduces MemPool, an elastic memory pool managing distributed memory
and KV caches across serving instances. Using MemPool APIs, MemServe combines
context caching with disaggregated inference for the first time, supported by a
global scheduler that enhances cache reuse through a global prompt tree-based
locality-aware policy. Tests show that MemServe significantly improves job
completion time and time-to-first-time.