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How much electricity do data centers use?
There is no single figure that answers this question for every geography and accounting boundary. The latest U.S. estimate covered here is from Lawrence Berkeley National Laboratory (LBNL): data centers used an estimated 192 terawatt-hours (TWh) of electricity in 2024, or 4.7% of total U.S. electricity consumption. The 2025 update, published in 2026, covers servers, storage, network equipment, and infrastructure, and excludes cryptocurrency mining. Its last historical year is 2024 because the main IT shipment data extend only through the first three quarters of 2025. Read LBNL’s 2025 U.S. Data Center Energy Usage Report.
The International Energy Agency (IEA) estimates global data center electricity consumption at about 415 TWh in 2024, or 1.5% of global electricity consumption. That is a worldwide estimate, not an alternative measurement of U.S. consumption. Read the IEA’s Energy and AI analysis.
How much electricity could data centers use by 2030?
Forecasts depend on assumptions, so report their scenario labels and ranges rather than presenting a single number as certain.
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| Geography and source | Year and status | Electricity estimate | Scope and qualification |
|---|---|---|---|
| United States — LBNL, published 2026 | 2024 historical estimate | 192 TWh; 4.7% of U.S. electricity consumption | Servers, storage, networking, and infrastructure; excludes cryptocurrency mining. |
| United States — LBNL, published 2026 | 2030 reference case | 649 TWh | Compounded uncertainty bounds: 521–843 TWh. |
| Global — IEA, published 2025 | 2024 estimate | About 415 TWh; 1.5% of global electricity consumption | Global figure; do not compare it as though it were a U.S. total. |
| Global — IEA, published 2025 | 2030 base case | About 945 TWh | Base-case projection, not a measured outcome. |
The LBNL 2030 reference case is 649 TWh, with compounded uncertainty bounds of 521–843 TWh. The IEA’s global base case is about 945 TWh. These figures answer different questions because one covers the United States and the other the world. LBNL and the IEA both emphasize uncertainty: the IEA says, “There is substantial uncertainty both about data centre consumption today and in the future.” IEA, Energy and AI.
What should you check before comparing two estimates?
Use these checks for each figure, including values repeated in news coverage, policy debates, and forecasts.
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- Geography: Is the estimate for the United States, the world, a region, or one facility? Do not treat a national estimate and a global estimate as competing totals.
- Year and status: Is it a historical estimate, a current-year estimate, a forecast, or a scenario? A forecast for 2030 is not a measurement of current consumption.
- Metric: Check whether the source reports electricity consumption in TWh, power capacity in gigawatts (GW), or total primary energy. These measure different things and cannot be used interchangeably.
- System boundary: Find out whether the total includes servers, storage, networking, cooling, and other facility infrastructure, and whether cryptocurrency mining is included. For example, LBNL’s 2025 U.S. report includes IT equipment and infrastructure but excludes crypto mining.
- Method: Look for how the estimate treats equipment shipments, installed stock, power draw, utilization, idle power, cooling, and power usage effectiveness (PUE).
- Scenario and uncertainty: Distinguish a reference or base case from sensitivity cases and compounded high- and low-end bounds. Include the range when the source provides one.
- Publication vintage: Note when the data and assumptions were assembled. A newer report may revise historical values, so differences between editions do not automatically mean real-world consumption changed.
How does LBNL estimate U.S. data center electricity use?
LBNL uses a bottom-up model. It starts with equipment shipments and installed stock, estimates device electricity from equipment power and operational assumptions, then applies a modeled PUE to IT energy to estimate whole-facility electricity. As LBNL puts it, “The resulting electricity usage estimates in this Report are derived from a ‘bottom-up’ energy use model.” LBNL’s 2025 update.
This method is useful for estimating a national total when a complete meter reading of every facility is not the basis of the calculation. It also means the result depends on model inputs. A change in shipment data or operating assumptions can change an estimate even if the underlying fleet’s actual electricity use did not change by the same amount.
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Which assumptions can make estimates look misleading?
Equipment shipments and deployment
A bottom-up estimate depends on how many servers and accelerators are installed now or expected to be deployed. Different shipment data, or different assumptions about future installations, can move the total. LBNL tests lower equipment installations and more specialized graphics chips as separate sensitivity cases; these are alternative assumptions, not observed outcomes.
Power, utilization, and idle operation
A server’s rated wattage is not the same as its annual energy use. LBNL’s model accounts for rated and maximum power, utilization, and idle power when estimating average server wattage over time. If a model assumes higher utilization or higher idle draw, its estimate for AI server electricity can rise even without a change in the number of machines.
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Equipment lifetime and efficiency
Forecasts also depend on how long accelerators remain in service and how much hardware and software efficiency improves. LBNL tests a shorter assumed AI-chip lifetime in one sensitivity case. The IEA identifies efficiency, AI uptake, and bottlenecks as uncertain drivers in its scenario analysis. A forecast that assumes rapid efficiency gains may differ substantially from one that assumes faster deployment or longer operating lives.
Facility overhead and PUE
IT equipment is only part of a data center’s electricity use. Cooling and other facility systems add overhead. LBNL applies modeled PUE to IT consumption and reports that national average PUE changes over time. An estimate covering whole-facility electricity cannot be compared directly with one counting only IT equipment.
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Why can a newer report revise an older estimate?
Report vintage matters because models and their inputs change. LBNL revised past estimates in its 2025 update in part because reported GPU shipments and AI inference-server power assumptions changed. A comparison that places a revised historical estimate beside an older edition without identifying the publication year can make a modeling update look like a sudden change in actual electricity consumption. LBNL’s report describes the updated estimates and assumptions.
How to explain a comparison without overstating it
Attach the key qualifiers to every number, especially when quoting a forecast. A clear comparison identifies the source, publication year, geography, estimate year, metric, boundary, and scenario. For instance, say “LBNL’s 2026-published U.S. estimate for 2024, excluding cryptocurrency mining, is 192 TWh,” rather than “data centers use 192 TWh.” For forecasts, include the case and range: “LBNL’s U.S. 2030 reference case is 649 TWh, with compounded uncertainty bounds of 521–843 TWh.”
Do not infer that one estimate is wrong just because its number differs from another. First determine whether they cover the same place and years, count the same systems, use comparable methods, and refer to the same kind of result. The LBNL and IEA totals, for example, are useful side by side only when labeled U.S. and global, respectively.
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