The computing power demand of artificial intelligence is pushing data centers to the forefront of power supply. The International Energy Agency (IEA) estimated in its Energy and AI report released in April 2025 that the global data center electricity consumption in 2024 will be approximately 415 terawatt hours, accounting for about 1.5% of global electricity demand; Based on the current growth momentum, this number may nearly double by 2030, reaching approximately 945 terawatt hours, with AI related workloads being the main driving force.
For data center operators and cloud providers, there are roughly three main approaches to deal with it.
Firstly, ensure that energy efficiency indicators are implemented effectively. PUE (Power Usage Efficiency) remains the core indicator for measuring the energy efficiency of computer rooms, and advanced projects have reduced the annual average PUE to around 1.1; However, the heat dissipation pressure brought by high-density AI chips is approaching the limit of air cooling, and liquid cooling solutions are shifting from "optional" to standard in high-density computer rooms.
Secondly, save power on the computing side. More efficient chips, more reasonable model scheduling, quantification and batch processing optimization in the inference stage can significantly reduce the energy consumption per unit of computing power. For operators, 'saved electricity' often comes faster and cheaper than building new power plants.
Thirdly, reconstruct the structure of power supply and energy consumption. More and more data centers are choosing to provide nearby green power, sign multi-year power purchase agreements, and use energy storage to smooth load curves; Some regions are still exploring the involvement of data centers in grid demand response, turning "electricity consumers" into flexible resources for the grid.
Electricity is not the background sound of AI development, but a hard constraint that determines how far computing power can go. People who are interested in AI should not only look at the model rankings, but also understand this energy ledger.
[Reference source] IEA "Energy and AI" (released in April 2025); The remaining content is comprehensively compiled from publicly released industry information.