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

OpenAI ’s fourth large language model (LLM), GPT-4 , took an estimated 50 gigawatt-hours to train, or the equivalent of 5,000 American homes ’ yearly power consumption. That was in 2023. Since then, the computational resources used to train frontier LLMs have only increased , though direct power usage numbers are hard to come by. Now, a research group at the University of Twente in the Netherlands has shown that you can save up to 14 percent of the energy used in LLM training without sacrificing speed by cleverly adjusting the clock frequency of the GPU during computation. Jeffrey Spaan , Ph.D. candidate at University of Twente and lead author on the article, presented the results at the Computing Frontiers conference in Catania, Sicily, last month. “My research is about finding computing waste,” Spaan says. “It’s similar to underutilization of the hardware, but instead of optimizing the software for the…