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Around half of the energy used to train AI can be saved without affecting speed or performance, using software developed at the University of Michigan Engineering. Now, U-M researchers are working with NVIDIA to integrate their new approach into Megatron-LM, NVIDIA's open-source platform for training AI.

Companies can use Megatron-LM directly or as a foundation to build private, customized training platforms. Megatron-LM is used to train NVIDIA's flagship Nemotron models, which users can download onto their own hardware.

By improving training efficiency, the U-M research team hopes to enable AI to do more useful work for less energy and cost. The savings come from finding the most efficient way to coordinate AI training across hundreds to thousands of computer processors, as well as how and when different jobs are performed.

"AI is becoming power-constrained as models become larger and more people want to train models, but we can achieve the same training speed and performance using less energy," said Ruofan Wu, a doctoral student in computer science and engineering at U-M who spent her summer integrating the U-M software into Megatron-LM.

"There are a lot of opportunities to optimize AI energy use at the software and hardware level, and one of the most rewarding parts of the internship was the opportunity to bring ideas from my research into production systems," she added.

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