SOURCE-LINKED INTELLIGENCE
Characterizing Job Power Elasticity for Power-Flexible AI Training
Large language model (LLM) training is among the fastest-growing sources of electricity demand in modern data centers, and power availability is a primary bottleneck to continued AI infrastructure growth. Making the power consumption of these workloads flexible could unlock additional power for AI growth, limit increases in electricity prices, and improve the utilization of existing grid infrastructure. However, to realize this flexibility, we must first understand how the performance of training workloads changes when GPU power is reduced. This paper presents the first systematic characteriza
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-10T13:40:13.000Z
First collected: 2026-09-20T19:02:05.452Z. This is not the publication date.