AI uses a lot of electricity because training and serving models concentrate demanding calculations on high-performance accelerated servers, then add the energy required for memory, storage, networking, cooling and power equipment. The International Energy Agency (IEA) estimates that all data centers—not AI alone—used about 415 terawatt-hours (TWh) in 2024, roughly 1.5% of global electricity consumption. In the IEA’s 2025 base case, that total reaches about 945 TWh in 2030, just under 3% of global electricity use. The global percentage is moderate, but large facilities are clustered in particular places, where they can become major local grid projects.
What is using the electricity?
An AI workload runs in a data center, so its electricity bill is a facility bill rather than a meter attached only to a model. The bill covers the computing devices that perform calculations, the equipment that moves and stores data, and the systems that keep all of it operating within safe temperature and power limits.
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- Servers: CPUs and, increasingly, accelerated servers containing GPUs or other specialized processors perform model training, fine-tuning and inference. The IEA says servers account for around 60% of electricity use in modern data centers on average, with the share varying considerably by facility type.
- Storage and networking: Training data, model weights, checkpoints and user requests must be stored and moved between machines. Storage arrays, switches and optical or electrical links therefore add to the load even when a processor is not doing arithmetic.
- Cooling: Chips turn electrical energy into heat. Air- or liquid-cooling equipment removes that heat so servers can run reliably; its demand rises with the amount and density of computing hardware.
- Power infrastructure: Power-distribution equipment, uninterruptible power supplies (UPS), transformers and backup systems consume energy and create operating overhead in addition to the IT load.
Because these components operate together, multiplying a chip’s rated power by the number of chips will understate facility consumption. Conversely, assigning every data-center kilowatt-hour to AI would overstate AI’s share: conventional computing, storage, networking and non-AI digital services remain part of the total.
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Why AI increases demand faster than conventional workloads
Accelerated servers concentrate more computation
Many AI models require very large numbers of parallel mathematical operations. Accelerated servers are designed to perform those operations efficiently, but they generally contain more high-power silicon and more memory bandwidth than a conventional server. Deploying them at scale raises the electrical load per rack and can force changes to cooling and power distribution.
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The IEA’s 2025 base case models electricity consumption in accelerated servers growing by about 30% per year. Consumption from conventional servers grows by about 9% per year in the same case. The faster category is mainly associated with AI adoption, although it is not identical to “AI electricity”: accelerated hardware can serve other high-performance workloads, and AI can run on more than one type of machine.
Training and inference create recurring demand
Training a model is an intensive, extended computation that repeatedly processes large data sets. Once a model is deployed, inference runs whenever a service answers a request, generates an image, ranks content or performs another prediction. A single training run may be temporary, while inference can continue for as long as a product has users. Both activities increase demand for accelerated servers, and both also require data movement, storage and cooling.
The amount of electricity per task is not fixed. It depends on model size, the number of users, response length, hardware utilization, software efficiency and how often a provider retrains or updates a model. This is why a projection of total data-center electricity cannot be converted into a universal “energy per prompt” figure.
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How the IEA allocates projected growth
In the IEA base case, accelerated servers account for almost half of net global data-center electricity growth through 2030. Conventional servers contribute around 20%, other IT equipment around 10%, and cooling plus other infrastructure around 20%.
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| Source of net growth through 2030 | IEA base-case contribution | How to interpret it |
|---|---|---|
| Accelerated servers | Almost 50% | A modeled category driven mainly by AI adoption; not a measurement of every AI kilowatt-hour. |
| Conventional servers | About 20% | Continued growth in ordinary compute workloads. |
| Other IT equipment | About 10% | Storage, networking and related information-technology equipment. |
| Cooling and other infrastructure | About 20% | Facility systems needed to operate the expanding IT load. |
These percentages describe modeled contributions to growth, not the percentage of the entire data-center electricity bill caused by AI. The IEA summarizes the broader trend this way: “AI is the most important driver of this growth, alongside growing demand for other digital services.”
How large is the projected increase?
The most useful figures are labeled by geography, year and scenario. The following estimates are not interchangeable: the IEA number is global, while the U.S. Department of Energy (DOE) number covers the United States and uses a different model.
| Estimate | Geography and year | Scenario or status | Electricity use |
|---|---|---|---|
| IEA historical estimate | All data centers, global, 2024 | Estimated actual consumption | About 415 TWh, approximately 1.5% of global electricity |
| IEA projection | All data centers, global, 2030 | 2025 base case | About 945 TWh, a little under 3% of global electricity |
| DOE projection | All data centers, United States, 2030 | Reference case | 649 TWh |
| DOE targeted sensitivities | United States, 2030 | Alternative assumptions | 578–782 TWh |
| DOE compounded uncertainty range | United States, 2030 | Combined uncertainty scenarios | 521–843 TWh |
The IEA also reports that data-center electricity use grew by around 12% per year over the previous five years. Its 945 TWh figure is a modeled base case, not a guaranteed outcome. AI adoption, hardware and software efficiency, the pace of deployment, supply-chain constraints and limits in the energy system could all push actual consumption above or below that path.
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DOE’s estimate is built with a bottom-up U.S. model that considers planned IT-equipment shipments, device electricity use, cooling simulations, facility types and locations. Its range reflects uncertainty in those inputs. A U.S. 649 TWh estimate cannot be added to the IEA’s global 945 TWh projection, and the two values should not be compared as though they measure the same territory or use identical assumptions.
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Why a modest global share can still be a serious local problem
Data centers are not spread evenly across the world. Cloud regions, fiber routes, available land, favorable taxes and access to electricity encourage operators to build clusters of facilities. A national or global average can therefore hide a large new load on one transmission corridor, substation or distribution network.
The IEA says data centers contribute less than 10% of global electricity-demand growth from 2024 to 2030 in its base case. That does not mean every grid can absorb them easily. A single hyperscale, AI-focused data center can have capacity of 100 megawatts (MW) or more. The IEA compares that scale with the annual electricity consumption of 100,000 households, using its stated facility and household assumptions. The comparison is illustrative and should not be generalized to every data center.
For a utility or local authority, the practical questions are therefore regional:
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- How long will substations, transformers and transmission upgrades take?
- Will several projects reach operation at the same time, creating a larger combined peak?
- What generation and storage resources can provide electricity when the facilities run continuously?
- How will connection costs and reliability obligations be allocated?
These are integration and planning questions, not proof that AI has consumed a fixed share of a particular community’s electricity. The answer varies with the facility’s load profile, location and connection arrangements.
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Where the electricity may come from
The IEA projects renewables to be the fastest-growing electricity source for data centers through 2030. In its supply analysis, renewable generation rises at an average annual rate of 22% from 2024 and meets nearly half of data-center demand growth. Natural gas, coal and nuclear also contribute, with substantial regional differences.
“Nearly half of demand growth” is not the same as “nearly half of all data-center electricity” and does not mean a new facility automatically runs on clean power. A data center draws from the electricity system to which it is connected. The marginal generation serving that load depends on local resources, contracts, transmission constraints and the time of day. One region may add wind, solar, hydro or nuclear capacity; another may rely more heavily on gas or coal while new plants and transmission are built.
Operators and policymakers consequently need to distinguish between a renewable-energy purchase, the physical electricity delivered at a site and the broader generation mix supporting the grid. Those concepts can produce different emissions and reliability outcomes even when they are described with similar language.
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Higher rack and facility power density
Accelerated servers can place more electrical demand in a smaller physical area than conventional systems. That raises the rating required for racks, busways, UPS equipment, transformers and backup generation. It also makes heat removal a design constraint rather than a secondary operating detail. The exact solution depends on the hardware configuration and facility type; the IEA’s 60% server-share figure is an average, not a design rule for every site.
Cooling becomes part of the compute decision
Every watt used by a processor eventually becomes heat that must be removed. As AI deployments increase power density, a cooling system can consume a meaningful portion of the facility’s electricity. The IEA groups cooling and other infrastructure at about 20% of net growth through 2030 in its base case, although the share varies by site and workload.
Efficiency changes the trajectory but does not guarantee lower totals
More efficient chips, better model architectures, software optimization and higher server utilization can reduce electricity per computation. The IEA and DOE scenarios still show uncertainty because efficiency improvements interact with demand: lower cost per task can encourage more tasks, larger models or wider deployment. A fall in energy per inference therefore does not automatically produce a fall in total data-center electricity.
How to read claims about AI electricity
Before accepting a statistic, check four labels:
- Geography: Is it global, national or a local grid area?
- Year: Is it a historical estimate or a future projection?
- Scenario: Is it a base or reference case, a sensitivity, or a compounded uncertainty range?
- Measure: Does it count electricity consumed inside data-center facilities, or generation supplied to them?
Also check what “AI” means in the statement. A figure for accelerated servers is a modeled equipment category. It is useful evidence that AI is driving growth, but it is not a census of all AI workloads and should not be presented as one.
What the projections do—and do not—establish
- They establish that data-center electricity use is growing and that AI-led deployment of accelerated servers is a major driver.
- They establish a large difference between global totals and the local impact of concentrated facilities.
- They provide scenario-based estimates, not a certain 2030 outcome.
- They do not provide one directly comparable, worldwide measurement of AI-only electricity consumption.
- They do not show that all new demand will be met by renewable generation.
- They do not imply that every data center has a 100 MW load or that every community will experience the same grid effects.
The practical bottom line for grids and operators
AI is changing the composition and density of data-center demand, not creating a separate electricity system. The near-term challenge is to expand computing capacity while planning the supporting cooling, power equipment, generation and transmission. Globally, the IEA’s base case puts data centers at just under 3% of electricity use in 2030; locally, a few large facilities can be large enough to reshape connection plans and investment schedules.
The most defensible way to discuss the trend is to use the IEA’s global figures and the DOE’s U.S. figures with their labels intact, separate accelerated-server growth from total AI consumption, and treat every 2030 number as scenario-dependent. That framing explains both why AI electricity demand is rising quickly and why its consequences will be felt first in the places where data centers cluster.
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