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Energy-Aware Scheduling for Serverless LLM Serving on Shared GPUs
As LLM inference grows as a cloud workload, its increasing energy use calls for cluster-wide optimization. Serverless LLM serving shares GPU resources elastically but complicates energy management due to multiple models running under a single device-wide operating point.
Optimizing energy consumption in shared GPU environments is crucial to managing the environmental and operational costs of large-scale LLM inference. Effective scheduling can help balance resource demands and energy efficiency in serverless platforms.
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Score 83
Source Type arxiv
Reposts 0
Topic Quality 62
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