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An AI data center is a facility packed with servers and supporting equipment that train and run AI models. It uses so much electricity because those servers perform enormous amounts of computation, while cooling systems remove the heat they produce and other equipment keeps them connected and running reliably. The challenge is not only how much electricity these facilities use over a year: their large, concentrated and sometimes rapidly changing demand can also strain local power systems.

What is an AI data center?

A data center is a building or campus that houses racks of servers, storage systems, networking equipment and the infrastructure that supports them. Servers process and store information. AI workloads run on these servers, often using specialized accelerators such as graphics processing units (GPUs) alongside central processing units (CPUs). The International Energy Agency (IEA) describes these facilities and their equipment in its overview of data-center energy demand.

An “AI data center” is not a single standardized facility type. The term generally refers to a data center designed or equipped to handle substantial AI workloads, especially with high-performance accelerated servers. Its power needs depend on the work being done, the number and type of servers, the site’s size, cooling efficiency and access to electricity.

Which parts use electricity?

Servers do most of the computing, but they are only part of a facility’s electrical load. Electricity also powers cooling, storage, networking and systems that convert and distribute power. Backup equipment helps maintain service when the normal supply is interrupted.

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  • Servers: CPUs and AI accelerators carry out calculations. In modern data centers, servers use around 60% of electricity on average, according to the IEA; the share varies between facilities.
  • Cooling: Servers turn nearly all the electricity they consume into heat, which must be removed to keep equipment within operating limits. Cooling accounts for about 7% of electricity in efficient hyperscale centers and can exceed 30% in less-efficient enterprise centers, according to the IEA.
  • Storage and networking: Storage systems account for around 5% of use on average, while networking can account for up to 5%, according to the IEA. Their shares depend on the facility and workload.
  • Power and backup systems: Power-conversion equipment supplies electricity to IT systems. UPS batteries and backup generators provide resilience, but the IEA says these backup systems are rarely used.

These are broad estimates, not a fixed recipe for every data center. A facility’s workload, scale, design and efficiency all affect how its electricity is divided among components.

Why do AI workloads need so much power?

Accelerated servers use more power per unit of space

AI training and deployment rely heavily on high-performance accelerated servers. Packing more computing capability into a facility raises power density: more electricity must be delivered to equipment in a given area, and more heat must be removed from that area. The IEA’s 2025 analysis projected that electricity use from accelerated servers, mainly driven by AI adoption, would grow faster than use from conventional servers and account for almost half of the net increase in data-center electricity demand through 2030 in its base case.

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AI demand can change quickly

AI model training and use can produce rapid swings in power demand. The IEA reported in 2026 that the power density of AI servers increased 11 times between 2020 and 2025 and was set to increase a further fourfold by 2027. Those are changes in server power density, not a forecast that every data center’s total electricity use will rise at the same rate. Rapid changes matter because the facility and power system must be able to deliver electricity reliably as demand shifts.

Cooling and other infrastructure add to the load

More computation means more heat. Cooling systems and other facility equipment therefore consume electricity in addition to the power used by servers. Better hardware, software and cooling efficiency can reduce the electricity needed for a given amount of work, but a more efficient facility can still use more power overall if it runs substantially more or more intensive workloads.

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How much electricity do data centers use?

The IEA’s published figures have changed as it has added newer data and updated its outlook. The numbers below come from different report vintages, so they should not be treated as one unchanged forecast.

IEA report Global data-center electricity use What the figure means
2025 report 415 TWh in 2024; around 1.5% of global electricity IEA estimate for the measured year 2024.
2025 report 945 TWh in 2030 IEA base-case projection, not a measured result.
2026 update 485 TWh in 2025 IEA reported global data-center electricity consumption for 2025, a 17% increase from 2024.
2026 update 950 TWh in 2030 IEA’s updated projection; it is a newer outlook than the 2025 report’s base case.

The 2025 and 2026 figures and outlooks are reported in the IEA’s Energy and AI report and its executive summary. The IEA also said AI-focused data-center electricity consumption grew 50% in 2025. That figure concerns AI-focused centers, not all data centers.

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For scale, the IEA’s 2025 report compared a typical AI-focused data center’s electricity consumption with that of 100,000 households. It said the largest facilities then under construction would use 20 times as much. These are IEA analogies, not universal measures of facility size; household consumption varies by location and the source does not define a typical center as a fixed technical category.

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Why can a large data center affect its local grid?

Globally, data centers accounted for around 1.5% of electricity use in 2024, and the IEA’s 2025 base case projected their share would remain below 3% in 2030. That global share does not mean the effects are evenly spread. Large facilities can cluster in particular places, concentrating demand on local generation and electricity networks. A new facility may require a large connection and dependable supply precisely where grid capacity is limited.

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Power systems also take time to expand. The IEA’s 2025 executive summary estimated that around 20% of planned data-center projects could be at risk of delays if grid risks were not addressed. It cited long connection queues and multi-year lead times for transmission construction. This was the IEA’s assessment in that report, not a prediction that one in five projects everywhere will be delayed.

The engineering challenge includes peak delivery as well as annual energy. Rapid power swings during AI training and model use can make reliable supply and equipment capacity important. Potential responses include building generation and transmission, adding energy storage, improving hardware and software efficiency, and operating or siting facilities more flexibly. Which measures are suitable depends on local grid conditions and the facility’s needs.

What is known—and what remains uncertain?

Recent electricity-use estimates describe past consumption; future numbers are projections that depend on assumptions. The IEA’s 2030 figures differ between its 2025 report and 2026 update. Future demand can change with AI adoption, hardware and software efficiency, investment decisions and energy-sector bottlenecks. The IEA considers multiple scenarios because those factors are uncertain; a projection should not be read as a guaranteed outcome.

Data centers are one of several drivers of rising electricity demand, not the sole cause of global growth. Their importance comes from the combination of expanding computation, high power density and concentrated local loads. Those characteristics make planning for electricity supply, grid connections, cooling and flexibility central to where and how AI infrastructure can grow.

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