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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.

Topic: LLM Inference Source: arXiv · arxiv.org Published 2026-06-29 14:44 UTC Fetched 2026-06-30 09:19 UTC

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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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