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How much electricity data centers use
The LBNL/DOE report is the reference point for national scale. Its figures are estimates and projections based on modeling and stated assumptions, and they describe the United States only.
| Measure | Value | Scope and qualification |
|---|---|---|
| U.S. data center use, 2024 | 192 TWh | Modeled national estimate in LBNL/DOE United States Data Center Energy Usage Report: 2025 Update |
| Share of U.S. electricity, 2024 | 4.7% | Share of total U.S. electricity consumption in the same report |
| Reference case, 2028 | 464 TWh | Projection, not an observed value |
| Reference case, 2030 | 649 TWh | Projection, not an observed value |
| Reference case share, 2030 | 11.8% | Share of forecast U.S. electricity use in the reference case |
| 2030 sensitivity range | 9.5% to 15.3% | Share of forecast U.S. electricity use across the report’s sensitivity cases, as summarized by DOE in Powering America’s AI Future: Data Center Resource Hub (2026) |
What the projections do and do not tell you
The 2030 sensitivity cases show how much the outcome depends on assumptions. The 2025 report gives sensitivity estimates ranging from 11% below to 21% above the reference case. It also says that compounded uncertainty could be roughly 20% below to 30% above. These are model uncertainty ranges. Unless the report defines them as statistical confidence intervals, do not describe them that way.
Two limits matter for any reader. First, a national total cannot tell you what a specific facility consumes. A campus, a colocation hall, and a hyperscale building can differ widely even when they serve similar customers. Second, the reference case is a scenario rather than a promise. The report stresses that industry trends, technology changes, and other external factors could move the outcome in either direction.
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Why demand is rising
According to the report, recent and projected growth is driven mainly by two trends: more accelerated servers (machines built around GPUs and similar processors) are being shipped, and each of those servers has a higher rated power.
The report also warns that efficiency gains do not guarantee lower total use. There are two different measures to keep apart:
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- Energy efficiency per computation measures how much work a chip or system completes per unit of energy. It can improve every year.
- Absolute electricity consumption measures how much power a facility or the nation draws in total. It can still rise if the amount of computing grows faster than efficiency improves.
A facility can become more efficient per computation and still draw more power overall. Most of the confusion in public discussion comes from treating these two measures as the same.
How operators can reduce demand
The report calls for continued work across six areas: chip design, IT hardware and system architecture, computing resource management and scheduling, facility power distribution, backup power systems, and cooling technologies. A useful way to read this list is in two halves. The first three areas reduce the energy needed for useful computation. The last three improve how the facility delivers power and removes heat. The report identifies these as areas to explore. It does not assign a fixed percentage saving to any of them.
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| Lever | Layer | What it affects | Savings figure |
|---|---|---|---|
| Chip design | IT side | Energy per computation | Not stated in the source |
| IT hardware and system architecture | IT side | Energy per computation | Not stated in the source |
| Computing resource management and scheduling | IT side | Energy per computation; can also change when loads run | Not stated in the source |
| Facility power distribution | Facility side | Losses between the grid connection and the equipment | Not stated in the source |
| Backup power systems | Facility side | Efficiency of backup power equipment | Not stated in the source |
| Cooling technologies | Facility side | Energy used to remove heat | Not stated in the source |
| Demand flexibility (shifting load timing) | Operations | When demand occurs, not necessarily total energy | Not stated in the source |
Sources: LBNL/DOE, United States Data Center Energy Usage Report: 2025 Update (2025), efficiency and flexibility sections.
IT-side measures
Chip design, hardware architecture, and workload management all sit closest to the computation itself. Reducing the energy each job requires lowers the load the facility must power and cool, so the benefit flows through the rest of the building. Scheduling decisions also determine how well servers are used, which affects how much idle hardware draws power.
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Facility-side measures
Power distribution, backup power, and cooling are where a facility’s own infrastructure converts grid electricity into usable computing power and removes the heat that results. Improvements here usually depend on measuring the existing system first. A cooling upgrade that does not match the actual heat load, for example, may not save much.
Demand flexibility
The report also recommends characterizing how data center demand varies over time and investigating flexibility, meaning shifting the timing of computing loads where operations allow. This is best understood as a way to serve more computing demand with a given set of energy resources. It is not a universal option. Latency-sensitive services and workloads that must run continuously generally cannot be moved without affecting service.
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A practical starting sequence for operators
DOE’s Federal Energy Management Program (FEMP) publishes data center energy-efficiency resources and design best practices, revised in 2024 for a range of scenarios. The sequence below follows the report’s logic and FEMP’s emphasis on measurement and engineering assessment.
- Measure total facility electricity and separate IT load from cooling and power-delivery losses over a representative period.
- Review IT utilization and identify idle or underused servers, along with workloads that could run more efficiently.
- Assess power distribution, backup systems, and cooling against the measured load, using FEMP’s design best practices.
- Decide which loads, if any, can move in time without affecting service, and only then evaluate demand flexibility.
- Re-measure after each change so the result reflects the facility’s own data rather than a national estimate.
FEMP’s guidance does not establish a guaranteed savings percentage for any operator. The actual result depends on the facility’s equipment, climate, workload, and design.
Choosing between options
When comparing measures, use five questions:
- Layer: Does it act on the IT equipment or on the facility infrastructure?
- Effect: Does it reduce total energy, or only shift the timing of load?
- Cost: What capital and operating requirements does it carry? The cited sources do not give comparative prices for individual technologies.
- Constraints: Will it affect reliability, latency, or the type of workload the facility serves?
- Evidence: Is the expected benefit based on a measurement of the facility, or only on a national modeled estimate?
Answering the first question with a national figure and the second with a sensible sequence of measurements gives an operator a far clearer basis for decisions than any single headline number.
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