A Year in LLM Serving: Workload Evolution, Caching and Load-Balancing
Large Language Model (LLM) serving has become a critical cloud workload, and realistic traces are essential for motivating and benchmarking serving systems. However, existing LLM serving workload studies remain limited in scale and scope. They often observe short time periods and provide limited visibility into how users interact with models in production. As a result, they do not fully capture how LLM serving workloads evolve over time or how user-model interactions shape production traffic. In this work, we further the understanding of real-world LLM serving workloads through both a global characterization and a longitudinal study of a one-year production trace from CompanyX. Unlike pri...
| Authors | William Nixon, Jon Durbin, Florian Standhartinger et al. |
|---|---|
| Primary category | cs.AI |
| Published | 3 July 2026 |
| Last updated at source | 7 September 2026 |
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