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arXiv · 2512.01644

A Systematic Characterization of LLM Inference on GPUs

Haonan Wang·Xuxin Xiao·Mingyu Yan·Zhuoyuan Zhu·Dengke Han·Duo Wang·Wenming Li·Xiaochun Ye·Cunchen Hu·Hongyang Chen·Guangyu Sun
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Abstract

This work presents a systematic characterization of Large Language Model (LLM) inference to address fragmented understanding. Through comprehensive experiments, we establish a four-dimensional analytical framework: (1) Two-Phase Heterogeneity Observation; (2) Microarchitectural Root Cause Analysis; (3) System Scaling Principles; and (4) Emerging Paradigm Boundaries. Our investigation progresses systematically from observation to foresight: identifying performance phenomena, revealing hardware causes, validating system behavior, and exploring new paradigms. This study not only consolidates a reliable empirical foundation for existing research but also provides new discoveries and practical optimization guidance for LLM inference.

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Haonan Wang, Xuxin Xiao, Mingyu Yan, Zhuoyuan Zhu, Dengke Han, Duo Wang, Wenming Li, Xiaochun Ye, Cunchen Hu, Hongyang Chen, Guangyu Sun. 2025-12-01. A Systematic Characterization of LLM Inference on GPUs. https://arxiv.org/abs/2512.01644

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