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AI data centers meet rising computing demand by coordinating accelerator-heavy servers, power delivery, cooling, and resilient utility connections. The challenge extends beyond the building: data centers can come online faster than the grid infrastructure that serves them, while their loads are often concentrated in particular regions. The scale of demand depends on the geography and forecast used: the International Energy Agency (IEA) estimates global use, while the U.S. Department of Energy (DOE) and Lawrence Berkeley National Laboratory (LBNL) model U.S. use.
How much electricity do data centers use?
Data centers worldwide used an estimated 415 terawatt-hours (TWh) of electricity in 2024, about 1.5% of global electricity use, according to the IEA’s 2025 Energy and AI analysis. Global data-center electricity demand had grown by about 12% annually over the preceding five years, the agency estimates.
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That global figure should not be confused with the U.S. estimate. In its 2025 update, published in 2026, DOE and LBNL estimate that U.S. data centers used 192 TWh in 2024, equal to 4.7% of total U.S. electricity. The estimates have different geographic scopes and are produced by different analyses; comparing their percentages does not show that one country’s facilities use a particular share of the global total.
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| Geography and source | 2024 estimate | 2030 outlook | How to read it |
|---|---|---|---|
| Global — IEA, 2025 | 415 TWh; about 1.5% of global electricity | Around 945 TWh in the Base Case | The Base Case is one of several modeled pathways, not a measured result or a certainty. The IEA also models Lift-Off, High Efficiency, and Headwinds cases. |
| United States — DOE/LBNL, 2025 update published 2026 | 192 TWh; 4.7% of U.S. electricity | 649 TWh, or 11.8% of forecast U.S. electricity, in the Reference Case; 521–843 TWh across the compounded uncertainty range | The range reflects uncertainty in assumptions and scenarios; the 11.8% uses forecast U.S. electricity as its denominator. |
The IEA’s global Base Case implies demand will nearly double between 2024 and 2030, growing around 15% per year over that period. Within that scenario, electricity use by accelerated servers—mainly driven by AI—is projected to grow around 30% annually; conventional-server electricity use grows around 9% annually. Accelerated servers account for almost half the modeled net increase in data-center demand. These are scenario projections, not observations.
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The U.S. range is wide because forecasts depend on assumptions including server shipments, accelerator counts, chip lifetimes, idle power, utilization, and AI inference demand. The DOE/LBNL update uses data available through late 2025 and extends its scenarios through 2030; its Reference Case is not a guaranteed outcome. See the DOE/LBNL 2025 Update for the U.S. estimates and modeling detail.
What makes an AI data center a high-density engineering problem?
A data center’s electricity use is not just the power consumed by its computing chips. The site must supply and condition electricity for IT equipment, remove the heat that equipment generates, and support reliable operation through power-conversion and backup systems. A typical facility can include servers, storage, networking, racks, cooling and environmental controls, uninterruptible power supply (UPS) batteries, backup generators, and a grid connection.
In the IEA’s broad estimates, servers account for around 60% of electricity demand in modern data centers on average, storage around 5%, and networking up to 5%. Cooling ranges from about 7% in efficient hyperscale facilities to more than 30% in less-efficient enterprise facilities. These are variable component estimates, not a fixed bill of materials or a prediction for every site.
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From accelerator deployment to utility demand
- Computing equipment sets the IT load. The mix and use of servers—including AI accelerators—shape how much electricity the racks require. Deployment and utilization matter, so installed capacity alone does not determine actual consumption.
- Electrical systems deliver and condition power. Power must reach the IT load through facility distribution and conversion equipment, with reliability provisions such as UPS batteries and backup generation. The required arrangement depends on the facility; the cited sources do not prescribe a universal design.
- Cooling removes equipment heat. More computing activity means more heat to manage. Cooling design therefore affects both the facility’s electricity use and, depending on the system and location, water use.
- The utility and grid must serve the resulting load. The project needs a connection capable of meeting its continuous demand, while the surrounding system must maintain reliability for other customers as well.
AI accelerators raise the importance of rack-level power delivery and thermal management, but the available estimates do not establish a universal rack-power threshold or a single cooling topology that suits every facility. A proposed rack density, cooling approach, and electrical design must be assessed against its equipment, operating profile, site, and utility connection—not inferred from an industry-wide average.
Why can a data center outpace the grid?
Data centers can be operational in two to three years, according to the IEA’s 2025 analysis, while planning and building broader energy infrastructure takes longer. That timing mismatch matters because a large facility needs firm, continuous power at a specific location; a global supply of electricity does not by itself ensure that a particular substation or transmission corridor can serve a new load.
DOE identifies load size, regional concentration, latency constraints, and the need for firm power as relevant characteristics for planning. Concentration can make the local effect significant even when data centers remain a modest share of electricity use at a global scale. Whether a proposed site can be served on its desired schedule is a local grid and interconnection question, not something a national or global forecast can settle.
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What can help align new loads with grid capacity?
- Plan grid infrastructure earlier. Utilities and developers can coordinate forecasts, interconnection needs, and build schedules before a facility is ready to operate.
- Expand and diversify supply. New grid infrastructure and clean generation can contribute to meeting demand, subject to location, timing, and system requirements.
- Use storage and flexible operations where feasible. Storage and the ability to shift or modulate some demand can help manage system needs, but they do not remove the requirement to establish a reliable service plan for the facility.
- Improve efficiency. More efficient computing and cooling can reduce the electricity required for a given level of service.
- Review interconnection and tariff arrangements. Planning and rate structures can be adapted to address large, concentrated loads and their implications for the wider system.
These are complementary planning options, not a guarantee that every location can accommodate any proposed project on the same timetable. DOE’s discussion of approaches is in Clean Energy Resources to Meet Data Center Electricity Demand.
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How do cooling choices affect water use?
Cooling has both an electricity cost and a water dimension. The IEA’s estimates show why facility type and efficiency matter: cooling accounts for about 7% of electricity use in efficient hyperscale facilities but can exceed 30% in less-efficient enterprise facilities. Those electricity shares do not, by themselves, establish how much water a site uses.
LBNL’s U.S. modeling accounts for water used onsite for cooling and water used indirectly to generate electricity. Its bottom-up approach combines computing-equipment shipment data with thermodynamic cooling-system modeling, and estimates water use by location under different cooling designs and power-supply scenarios. For a meaningful site comparison, distinguish direct onsite water from upstream water associated with electricity generation, and keep the location and system boundary clear. The available material does not support a universal water-per-computation figure or a general ranking of air, evaporative, and liquid cooling for every facility. LBNL describes its approach on its U.S. Data Center Energy & Water Modeling & Forecasting page.
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How to assess a data-center power claim or proposal
Before comparing a forecast, project announcement, or efficiency claim, check what it measures. The following distinctions often explain why apparently similar numbers do not match:
- Geography: Is the figure global, national, regional, or specific to a utility service area?
- Time and evidence type: Is it historical estimated use or a forecast for a stated year?
- Scenario: Is a forecast a reference or base case, a sensitivity case, or a range spanning multiple assumptions?
- Facility type: Does it describe enterprise, colocation, or hyperscale facilities, whose component shares can differ?
- Electricity boundary: Is the number for IT equipment alone or the whole facility, including cooling and other infrastructure?
- Water boundary: Does it count onsite cooling water, water used to generate electricity, or both?
- Site readiness: Can the local grid provide the required capacity, reliability, and timing, and is any flexibility assumed?
Percentages need their denominators and years, too: 4.7% of U.S. electricity in 2024 and 11.8% of forecast U.S. electricity in 2030 are not interchangeable with a global share or with a facility’s IT-load percentage. The IEA’s Key Questions on Energy and AI, published April 16, 2026, provides additional context on the evolving relationship between AI and energy.
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