For immediate impact, authors of the report wrote, “the Secretary should convene energy utilities, data center developers and operators, and other key stakeholders to start active dialog on how to address current electricity supply bottlenecks,” as well as “to develop strategies for how to generate and deliver the power needed to sustain AI leadership into the future.”
The recommendation to create a test bed, said Beran, “is really step one for the DoE in terms of understanding what infrastructure is being used, and how much energy it consumes. And then, once we have this starting data point, how do we improve from there? This really made me think of the old saying, ‘you can’t improve on what you can’t measure.’ They have to start somewhere and set a base case and that to me, is what this is.”
Developing solutions
He said the hyperscalers, to which the working group reached out to solicit views, face “unsolved problems in how to manage power demands from AI workloads. It is not like the industry has solved the problems or challenges, it is more like, ‘we have identified challenges with the AI workload and energy profile requirements, and now we need to start to develop some solutions for them.’”
Those solutions, said Beran, range from changing how data center facilities are architected to making system changes to accommodate the workload profile.
Of note, he said, is the need to improve the energy efficiency factor. He added that while sustainability has “been such a critical factor the past couple of years, it is really started to take a backseat to some of the AI growth requirements. Trying to manage both is important.”
In addition, Thomas Randall, director of AI market research at Info-Tech Research Group, said via email, “as AI models get larger and require more compute power, the amount of energy required to support this market will equally increase. Without a broader energy strategy, countries that house the data centers AI companies are using will face ongoing CO2 emission issues, limitations on growth, and opportunity costs for energy use elsewhere.”
