{"ID":2834495,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.01644","arxiv_id":"2512.01644","title":"A Systematic Characterization of LLM Inference on GPUs","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.","short_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 P...","url_abs":"https://arxiv.org/abs/2512.01644","url_pdf":"https://arxiv.org/pdf/2512.01644v1","authors":"[\"Haonan Wang\",\"Xuxin Xiao\",\"Mingyu Yan\",\"Zhuoyuan Zhu\",\"Dengke Han\",\"Duo Wang\",\"Wenming Li\",\"Xiaochun Ye\",\"Cunchen Hu\",\"Hongyang Chen\",\"Guangyu Sun\"]","published":"2025-12-01T13:16:31Z","proceeding":"cs.AR","tasks":"[\"cs.AR\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}