AI Research
A Year in LLM Serving: Workload Evolution, Caching and Load-Balancing
Unveiling one year of production traces to redefine LLM infrastructure efficiency and load-balancing strategies.
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...
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ArXiv CS.AI
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ArXiv CS.AI
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This longitudinal study provides the most comprehensive dataset to date for modeling real-world LLM production traffic patterns. By exposing the granular dynamics of user-model interactions, it enables researchers to build more efficient caching and load-balancing architectures.
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