FineServe Dataset Characterizes Global LLM Serving Workloads
Summary
Researchers introduce FineServe, a new dataset capturing real-world, multi-model LLM serving dynamics from a global commercial marketplace. This dataset enables fine-grained analysis of arrival patterns and token behavior across diverse LLM architectures and tasks.
Why it matters
Professionals deploying or managing LLM services can use these insights and tools to better understand real-world demands, optimize resource allocation, and improve the efficiency and reliability of their serving infrastructure.
How to implement this in your domain
- 1Analyze existing LLM serving logs against FineServe's characterizations to identify similar patterns and potential bottlenecks.
- 2Utilize the FineServe workload generator to benchmark current or prospective LLM serving platforms under realistic, multi-model load conditions.
- 3Develop adaptive routing and scheduling algorithms that account for the fine-grained fluctuation regimes identified in the research.
- 4Refine capacity planning strategies by incorporating insights into model-specific demand dynamics and saturation points.
Who benefits
Key takeaways
- FineServe provides the first fine-grained, real-world dataset for LLM serving workloads.
- LLM serving dynamics vary significantly across model architectures, scales, and task intents.
- The FineServe workload generator enables realistic benchmarking of multi-model LLM serving platforms.
- Understanding these dynamics is critical for efficient routing, scheduling, and capacity planning.
Original post by Tiancheng Zhang, Shaoyuan Huang, Mingyuan Wang, Yunfeng Zhao, Xiaofei Wang, Wenyu Wang
"arXiv:2607.19349v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as always-on online services, making efficient LLM serving a critical systems challenge. Achieving low latency and high throughput under volatile demand requires deep understand…"
View on XPrimary sources
Originally posted by Tiancheng Zhang, Shaoyuan Huang, Mingyuan Wang, Yunfeng Zhao, Xiaofei Wang, Wenyu Wang on X · view source
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