Timing Trick Cuts Energy Used in LLM Training by Up to 14 Percent

Jul 10, 2026

Researchers at the University of Twente have demonstrated that dynamic voltage and frequency scaling (DVFS) can reduce energy consumption in large language model (LLM) training by up to 14% without sacrificing speed. The technique involves adjusting GPU clock frequencies during computation to minimize power draw. The findings were presented at the Computing Frontiers conference.

Why it matters: This method could help reduce the significant energy demands and environmental impact associated with training large AI models.

Full story at: IEEE Spectrum / AI ↗