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: when GPUs are bound to stall, PAFT opportunistically reduces their clock frequencies to match the ...
| Authors | Miguel Braga, Júlio Pinto, Rahma Nouaji et al. |
|---|---|
| Primary category | cs.DC |
| Published | 21 September 2026 |
| Last updated at source | 21 September 2026 |
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