AI Research
Calibrate, Then Route: A Measured Study of Learned Request Routing for Disaggregated LLM Serving
Optimizing request distribution to maximize efficiency in disaggregated large language model inference architectures.
Disaggregated LLM serving places compute heavy prefill and memory heavy decode on separate GPU pools. Systems such as DistServe, Splitwise, and Mooncake make this separation fast, but routing still determines which instances handle each request. We study a router that estimates t...
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ArXiv CS.AI
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ArXiv CS.AI
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Optimizing request routing in disaggregated LLM architectures is critical for balancing prefill compute and decode memory efficiency. This research provides a data-driven framework that minimizes latency bottlenecks in distributed serving environments.
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