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Medusa: Optimizing Serverless LLM Inference by tanwei is a document available to read on EtoBox.

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The document presents Medusa, a system designed to accelerate serverless large language model (LLM) inference by addressing the cold start latency caused by model loading and initialization stages. Medusa utilizes state materialization to pre-load CUDA graphs and KV cache information offline, significantly reducing the loading phase latency by 42.5% and the time to first token by 53.0% under real-world workloads. The proposed techniques enhance the efficiency of serverless LLM inference, making it more scal

Author

tanwei

Language

EN