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A principled approach for energy-efficient training via phase-aware GPU frequency tuning
Modern AI model training imposes unprecedented computational demands, making it a key contributor to datacenter energy consumption. Yet a significant fraction of the energy consumed during training does not translate to useful computation due to bottlenecks throughout the training pipeline. We present PAFT, a phase-aware, dynamically adaptable GPU frequency tuning system that reduces energy consumption of training workloads with minimal performance overhead. The key insight behind PAFT is that bottlenecks represent an energy optimization opportunity, rather than purely a performance problem: w
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-21T07:20:57.000Z
First collected: 2026-09-23T08:01:43.213Z. This is not the publication date.