BurstGPT: A Real-World Workload Dataset to Optimize LLM Serving Systems
Yuxin Wang, Yuhan Chen, Zeyu Li, Xueze Kang, Yuchu Fang, Yeju Zhou, Yang Zheng, Zhenheng Tang, Xin James He, Rui Guo, Xin Wang, Qiang Wang, Amelie Chi Zhou, Xiaowen Chu · 2025
Despite efforts to improve the quality of service (QoS) and throughput in Large Language Model (LLM) serving systems, progress is often limited by the lack of publicly available real-world workloads.Consequently, evaluations usually depend on synthetic or oversimplified load patterns, and systems that appear promising in testing frequently underperform once deployed.This work presents BurstGPT, an LLM serving workload with 10.31 million traces from regional Azure OpenAI GPT services * Both authors contributed equally to this research.