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Large Language Models (LLMs) have recently experienced great success, asevident in the widespread popularity of ChatGPT. Existing LLM serving systemsare stateless across requests. Consequently, when LLMs are used in the commonsetting of multi-turn conversations, a growing log of the conversation historymust be processed alongside any request by the serving system at each turn,resulting in repeated history processing. In this paper, we design $Pensieve$,a system optimized for multi-turn conversation LLM serving. $Pensieve$maintains the conversation state across requests by caching previouslyprocessed history to avoid duplicate processing. $Pensieve$'s multi-tiercaching strategy can utilize both GPU and CPU memory to efficiently store andretrieve cached data. $Pensieve$ also generalizes the recent PagedAttentionkernel to support attention between multiple input tokens with a GPU cachespread over non-contiguous memory. Our evaluation shows that $Pensieve$ is ableto achieve 1.51-1.95x throughput compared to vLLM and reduce latency by 60-75%.

Stateful Large Language Model Serving with Pensieve