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.
@article{arxiv.2503.08415,
title = {TokenSim: Enabling Hardware and Software Exploration for Large Language Model Inference Systems},
author = {Feiyang Wu and Zhuohang Bian and Guoyang Duan and Tianle Xu and Junchi Wu and Teng Ma and Yongqiang Yao and Ruihao Gong and Youwei Zhuo},
journal= {arXiv preprint arXiv:2503.08415},
year = {2025}
}