A ChatGPT(GPT-3.5) & GPT-4 Workload Trace to Optimize LLM Serving Systems

Note

✨ Traces with new columns Session ID and Elapsed time are available now!

This repository contains public releases of a real-world trace dataset of LLM serving workloads for the benefit of the research and academic community.

This LLM serving is powered by Microsoft Azure.

Main characteristics

BurstGPT_1 & BurstGPT_2

• Duration: 121 consecutive days in 4 consecutive months.
• Dataset size: ~5.29M lines, ~188MB.

BurstGPT_3

• Duration: 110 consecutive days in 4 consecutive months.
• Dataset size: ~5.34M lines, ~220MB.

Data

There are currently 6 files in Release v2.0:

• BurstGPT_1.csv contains all of our trace in the first 2 months with some failure that Response tokens are 0s. Totally 1429.7k lines.

• BurstGPT_without_fails_1.csv contains all of our trace in the first 2 months without failure. Totally 1404.3k lines.

• BurstGPT_2.csv contains all of our trace in the second 2 months with some failure that Response tokens are 0s. Totally 3858.4k lines.

• BurstGPT_without_fails_2.csv contains all of our trace in the second 2 months without failure. Totally 3784.2k lines.

• BurstGPT_3.csv contains all of our trace in another 110 days with some failure that Response tokens are 0s. Totally 5344.0k lines.

• BurstGPT_without_fails_3.csv contains all of our trace in another 110 days without failure. Totally 4956.1k lines.

BurstGPT_1.csv is also in /data for you to use.

Schema

• Timestamp: request submission time, seconds from 0:00:00 on the first day (calibrated to the local time zone ).
• Session ID: conversation ID, only conversation mode have this, traces that share the same value of Session ID are in the same conversation session.
• Elapsed time: time between the request submission time and system response time, covers the full duration from submitting the inference request to getting the complete final response (all tokens included). It’s not the same as TTFT (time to first token). In seconds.
• Model: called models, including ChatGPT(GPT-3.5) and GPT-4.
• Request tokens: Request tokens length.
• Response tokens: Response tokens length.
• Total tokens: Request tokens length plus response tokens length.
• Log Type: the way users call the model, in conversation mode or using API, including Conversation log and API log.

Usage

1. You may scale the average Requests Per Second (RPS) in the trace according to your evaluation setups.
2. You may also model the patterns in the trace as indicated in our paper and scale the parameters in the models.
3. Check our simple request generator demo in example/. If you have some specific needs, we are eager to assist you in exploring and leveraging the trace to its fullest potential. Please let us know of any issues or questions by sending email to mailing list.

Paper

If the trace is utilized in your research, please ensure to reference our paper:

@inproceedings{BurstGPT,
  author    = {Yuxin Wang and Yuhan Chen and Zeyu Li and Xueze Kang and Yuchu Fang and Yeju Zhou and Yang Zheng and Zhenheng Tang and Xin He and Rui Guo and Xin Wang and Qiang Wang and Amelie Chi Zhou and Xiaowen Chu},
  title     = {{BurstGPT}: A Real-World Workload Dataset to Optimize LLM Serving Systems},
  booktitle = {Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (KDD ’25)},
  year      = {2025},
  address   = {Toronto, ON, Canada},
  publisher = {ACM},
  doi       = {https://doi.org/10.1145/3711896.3737413},
  url       = {https://doi.org/10.1145/3711896.3737413},
}

Data Overview (First 2 Months)

Figure 1: Weekly Periodicity in BurstGPT.

Figure 2: Daily Periodicity in BurstGPT.

Figure 3: Average Daily Request and Response Throughput in BurstGPT.

Figure 4: Statistics of Request and Response Tokens in BurstGPT.