EPRI, Epoch AI Joint Report Finds Surging Power Demand from AI Model Training

PALO ALTO, Calif. and BERKELEY, Calif., Aug. 11, 2025 /PRNewswire/ -- The rapid advancement of artificial intelligence—particularly the training of large-scale models that are used to power many of today's widely used applications—is driving renewed growth in electricity demand. A new report released today from EPRI and Epoch AI forecasts that training a leading model could require more than 4 gigawatts (GW) of power by 2030—enough to power millions of U.S. homes.

The training of large-scale AI models has historically required large, localized power supply. Despite rapid efficiency gains, the power demands of training a leading model have more than doubled every year for the past decade, according to the report. AI companies have found that increasing model size and complexity can provide better performance, which in turn drives the need for additional compute and electrical power. The report finds that the AI industry will likely continue to scale up its models in the coming years, despite recent computational efficiency breakthroughs.

Overall power demand for AI extends well beyond large-scale training. Significant power capacity will be allocated towards the deployment of AI to serve users, as well as training smaller models and conducting AI research. Total AI power capacity in the U.S. is estimated at around 5 GW today and could reach more than 50 GW by 2030—matching the total global demand from data centers today and comprising a rapidly growing share of overall data center power demands.

"The energy demands of training cutting-edge AI models are doubling annually, soon rivaling the output of the largest nuclear power plants," said Jaime Sevilla, director of Epoch AI. "This report offers a rigorous, data-driven look at these trends and where they're headed. Epoch AI will continue investigating the energy demand of AI and related topics."

"AI applications are becoming prevalent in our daily lives and will likely play a key role in the energy system of the future," said EPRI President and CEO Arshad Mansoor. "To meet these rising energy demands, data center developers and power providers are embracing innovative solutions in a build-to-balance approach. Building new infrastructure along with balancing through flexibility in data center design will be critical to accelerate grid connections, while minimizing costs and enhancing system reliability," he added.

EPRI launched the DCFlex collaborative last year to demonstrate the technologies, policies, and tools to make data center flexibility a reality. Data center flexibility, including geographically distributed training data centers, could transform data centers from passive customers to grid assets to improve reliability, lower costs, and speed connection. The effort, which brings together over 45 companies, including founding members Google, Meta, NVIDIA, and various utilities, recently launched its first real-world field demonstrations in Lenoir, N.C., Phoenix, Ariz., and Paris, France.

To read the full joint report, visit: Scaling Intelligence: The Exponential Growth of AI's Power Needs

Contact:

Rachel Gantz
EPRI
Senior Manager of Corporate Media Relations
202-293-7517
[email protected]

Caroline Falkman Olsson
Epoch AI
940-601-6412
[email protected] 

About EPRI
Founded in 1972, EPRI is the world's preeminent independent, non-profit energy research and development organization, with offices around the world. EPRI's trusted experts collaborate with more than 450 companies in 45 countries, driving innovation to ensure the public has clean, safe, reliable, and affordable access to electricity across the globe. Together...shaping the future of energy.^®

About Epoch AI
Epoch AI is a multidisciplinary research institute investigating the trajectory of artificial intelligence (AI) and the underlying forces driving AI development. Epoch AI is committed to grounding public discourse on AI in robust empirical evidence, by making its findings widely accessible through reports, models, and visualizations.

SOURCE EPRI