A 14 percent reduction in LLM training energy is possible by adjusting GPU clock frequencies during computation. Researchers at the University of Twente achieved these savings without sacrificing processing speed. This method targets computational waste in frontier models. Practitioners can now lower power overhead without compromising the training timeline of large-scale models.