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Research indicates that off-grid solar-plus-storage systems could effectively power data centers dedicated to training artificial intelligence models in the southwest U.S. These microgrids could meet a projected power demand of 15 GW to 150 GW by 2030. The study highlights the potential for 1200 GW of off-grid solar capacity, making it a viable option for reducing carbon emissions.
The findings suggest that microgrids with up to 90% solar energy could be economically feasible for AI training centers, which are more flexible in location compared to inference data centers. The study also discusses the competitive costs of these renewable systems compared to traditional energy sources, emphasizing the need for innovation in energy solutions for the growing AI sector.
• AI training data centers could utilize off-grid solar microgrids.
• Projected AI power demand ranges from 15 GW to 150 GW by 2030.
AI refers to the simulation of human intelligence in machines, particularly in training models for various applications.
A data center is a facility used to house computer systems and associated components, crucial for AI model training.
Microgrids are localized energy systems that can operate independently or in conjunction with the main power grid, ideal for powering AI data centers.
Paces specializes in identifying land for solar microgrids, contributing to the research on off-grid energy solutions for AI.
Scale Microgrids focuses on analyzing the levelized cost of energy for renewable systems, relevant for powering AI training facilities.
Stripe is involved in the research project, assisting with modeling and drafting the white paper on solar microgrids for AI.
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