Papers › TokenSim: Enabling Hardware and Software Exploration for Large Language Model Inference Systems

TokenSim: Enabling Hardware and Software Exploration for Large Language Model Inference Systems

11 Mar 2025arXiv:2503.08415links table onlyarchive 2025-07-28

Feiyang Wu, Zhuohang Bian, Guoyang Duan, Tianle Xu, Junchi Wu, Teng Ma, Yongqiang Yao, Ruihao Gong, Youwei Zhuo

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The increasing demand for large language model (LLM) serving has necessitated significant advancements in the optimization and profiling of LLM inference systems. As these models become integral to a wide range of applications, the need for efficient and scalable serving solutions has grown exponentially. This work introduces TokenSim, a comprehensive hardware and software exploration system designed specifically for LLM inference. TokenSim is characterized by its support for extensible system optimizations including scheduling and memory management. We validate the results with systems running with realworld datasets, achieving an error rate of less than 1%. Furthermore, TokenSim facilitates various insightful explorations into the performance and optimization of LLM serving systems.

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