Stateful Large Language Model Serving with Pensieve

Large Language Models (LLMs) are wildly popular today and it is important to serve them efficiently. Existing LLM serving systems are stateless across requests. Consequently, when LLMs are used in the common setting of multi-turn conversations, a growing log of the conversation history must be processed alongside any request by the serving system at each turn, resulting in repeated processing.

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Publication Details

Published
2025-03-26
DOI
https://doi.org/10.1145/3689031.3696086
Citations
8
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
19.85
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article

Stateful Large Language Model Serving with Pensieve

Lingfan Yu, Jinkun Lin, Jinyang Li
8 citations
Topic Modeling
19.85
article

Stateful Large Language Model Serving with Pensieve

Lingfan Yu, Jinkun Lin, Jinyang Li
article en
8 citations

Abstract

Large Language Models (LLMs) are wildly popular today and it is important to serve them efficiently. Existing LLM serving systems are stateless across requests. Consequently, when LLMs are used in the common setting of multi-turn conversations, a growing log of the conversation history must be processed alongside any request by the serving system at each turn, resulting in repeated processing.

New York University (US)
Openalex Percentile: Top 1%
Topic Modeling
19.85
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Stateful Large Language Model Serving with Pensieve — Lingfan Yu, Jinkun Lin, et al. · (2025) | TGRS Research Map | TGRS