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An AI data center is a facility filled with servers and supporting equipment that runs AI calculations and stores and moves data. It needs substantial electricity because AI servers perform intensive computing, and the electricity they use largely becomes heat that the facility must remove. Cooling, networking, power conversion and backup systems add to the load. Globally, data centers used an estimated 485 terawatt-hours (TWh) of electricity in 2025, according to the International Energy Agency (IEA); their concentrated demand can make local grid connections a bigger challenge than the global share alone suggests.
What is inside an AI data center?
A data center is not one giant computer. It is a building or campus containing rows of racks that hold servers, storage systems and networking equipment, alongside the power and environmental systems needed to keep them operating. The IEA describes these facilities as housing servers, storage, networking and auxiliary equipment. AI training and services that use trained models run mainly in data centers.
- Servers process calculations and store data. They use central processing units (CPUs) and, for many AI tasks, specialized accelerators such as graphics processing units (GPUs).
- Storage holds data and model files, while networking equipment moves information among servers and to users.
- Power systems convert incoming electricity into forms the equipment can use. Uninterruptible power supply (UPS) batteries and backup generators help keep systems available during outages; they are present for reliability but are rarely used.
- Cooling and environmental controls remove heat and maintain conditions suitable for the equipment.
AI-focused facilities rely on high-performance accelerated servers. That makes their computing equipment more power-dense: more electricity is needed in a given space than in a lower-density facility. The exact profile depends on workload, facility scale, server type, cooling efficiency and location.
Why does an AI data center use so much power?
Computing is the largest load
Training an AI model involves running large numbers of calculations across servers; using a model also requires computing resources. More or faster computation generally requires more electricity. In the IEA’s account of modern data centers, servers use about 60% of electricity on average, though the share varies among facilities.
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Nearly all IT electricity becomes heat
Electricity used by servers and other information technology (IT) equipment ultimately becomes heat. The facility must move that heat away to keep equipment within operating conditions, so cooling consumes additional electricity. The IEA estimates that cooling ranges from about 7% of total consumption in efficient hyperscale facilities to more than 30% in less-efficient enterprise facilities. Those figures describe a range, not a universal share.
Supporting systems add to the total
Storage systems account for around 5% of electricity use in the IEA’s modern-data-center breakdown, and networking can account for up to 5%. Power conversion and other facility systems also contribute. The shares vary, so a single percentage breakdown should not be treated as a fixed design rule for every AI data center.
How large is data-center electricity use?
The IEA’s published figures distinguish measured or estimated recent consumption from projections. Its reports give slightly different outlooks because they use different publication vintages; the numbers below should not be read as one unchanged forecast.
| Figure | What it describes | Status |
|---|---|---|
| 415 TWh; around 1.5% of global electricity | Global data-center electricity consumption in 2024 | IEA estimate published in 2025 |
| 945 TWh | Global data-center electricity consumption in 2030 | IEA 2025 report base-case projection |
| 485 TWh; demand grew 17% | Global data-center electricity consumption in 2025 and growth that year | IEA figures published in 2026 |
| 50% growth | Electricity consumption at AI-focused data centers in 2025 | IEA figure published in 2026 |
| 950 TWh | Global data-center electricity consumption in 2030 | IEA projection published in 2026 |
The 2025 report’s estimate and base case are described in the IEA’s Energy and AI demand analysis; the later consumption figure and updated projection are in its 2026 update. The 2030 figures are forecasts, not measured outcomes. Future demand depends on AI adoption, efficiency improvements, investment and energy-system bottlenecks.
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Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →In the IEA’s 2025 base case, electricity use by accelerated servers, driven mainly by AI adoption, grows faster than use by conventional servers and accounts for almost half of the net increase in data-center demand through 2030. Cooling and other infrastructure contribute too. This does not mean data centers account for most global electricity growth: the IEA identifies them as one of several drivers.
Why can the grid feel the impact locally?
Global totals can obscure where the electricity is needed. Large data centers often cluster in particular areas, concentrating demand on local generation and electricity networks. A region may therefore face a substantial connection or capacity challenge even when data centers remain a modest fraction of worldwide electricity use.
The IEA’s 2025 report said data centers represented around 1.5% of global electricity consumption in 2024 and projected their share would remain below 3% in 2030 in its base case. In that same report, it estimated around 20% of planned data-center projects could be at risk of delays if grid risks were not addressed. It pointed to long connection queues and multi-year construction lead times for transmission; this was an assessment of project risk, not a prediction that every project would be delayed. See the IEA’s executive summary.
The IEA has also compared a typical AI-focused data center’s electricity use to that of 100,000 households, and said the largest facilities under construction at the time could consume 20 times as much. This is an IEA analogy illustrating scale, not a universal size for AI data centers.
Why are peaks and power swings part of the problem?
Annual energy use, measured in kilowatt-hours or terawatt-hours, is only part of the engineering challenge. A facility also needs enough power available at the moments its equipment demands it. The IEA’s 2026 update says the power density of AI servers increased 11-fold between 2020 and 2025 and is set to increase a further fourfold by 2027. It also describes rapid power swings during AI training and model use. These swings make stable supply, equipment capacity and the ability to manage peaks important alongside total annual consumption. The IEA discusses these issues in its analysis of demand from AI.
What can reduce the strain?
There is no single fix, because demand, efficiency and grid constraints vary by facility and region. The IEA identifies several responses that can work together:
- Build or upgrade generation and transmission so new facilities can connect and receive power reliably.
- Use energy storage to help manage demand peaks and support reliable supply.
- Improve hardware and software efficiency so useful computing can be delivered with less electricity.
- Make operation or siting more flexible where workloads and grid conditions allow it.
IEA outlooks are scenario-based: the results vary with AI adoption, efficiency gains and energy-sector bottlenecks. A forecast is therefore not a guarantee of what consumption will be in 2030.
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