Artificial intelligence (AI) is poised to drive the next industrial revolution. Large language models alone attract millions of users weekly and process billions of prompts each day. Despite its proven utility across virtually every industry, the data centers powering these systems consume enormous amounts of electricity, placing considerable pressure on the electric grid.
As AI adoption accelerates, data center power consumption is projected to rise significantly. Meeting that demand by building new data centers is costly, both financially and in navigating regulatory requirements. At the same time, the rapid growth of AI computing is creating new challenges for energy providers trying to maintain grid stability.
Researchers at Carnegie Mellon University (CMU) are partnering with Bosch Research (Pittsburgh, PA) to explore how AI data centers can operate more efficiently by coordinating computing workloads with energy availability. Their project focuses on jointly optimizing AI job scheduling and energy use to reduce grid strain and increase renewable energy utilization.
“The world is going through a major AI revolution in regard to large models that are changing our daily lives,” says Guannan Qu, assistant professor of electrical and computer engineering at CMU. “These large models must run in data centers, which consume substantial energy. This, in turn, only feeds the interest to develop even more data centers to support additional models.”
