LLM system cuts data center energy use by 32%

Researchers have developed a new system using large-language models (LLMs) to improve the sustainability of data centers. This LLM-based predictive scheduling system analyzes source code to estimate execution time and energy consumption, with potential applications for water usage and carbon emissions. The system's scheduling algorithm then optimizes GPU resource allocation to reduce energy use and waiting times. In collaboration with a data center, this approach led to a 32% reduction in energy consumption and a 30% decrease in waiting time. AI

IMPACT Optimizes AI workload scheduling to reduce energy consumption and resource usage in data centers.

RANK_REASON Academic paper detailing a novel system for AI infrastructure optimization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• graphics processing unit
• Hugging Face
• IArxiv
• large-language models

• paper
• infra

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