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Why data-center electricity demand is rising
The International Energy Agency (IEA) estimates that data centers used around 415 terawatt-hours (TWh) of electricity worldwide in 2024—about 1.5% of global electricity consumption. In its 2025 base case, the IEA projects global use could reach around 945 TWh by 2030. That is a scenario, not a guaranteed outcome: the agency models uncertainty around efficiency improvements, AI uptake and bottlenecks in the energy sector.
AI is an important driver, but it is not the whole story. AI training and deployment rely on data centers, and accelerated servers—including systems using GPUs or application-specific integrated circuits (ASICs)—are contributing to demand. Conventional servers and facility infrastructure also consume electricity, and the balance varies between facilities. Servers are the largest electricity-consuming component on average in modern data centers, according to the IEA.
Efficiency gains do not necessarily mean lower total electricity use. More computing per unit of energy can be outweighed if demand for computing grows faster. The IEA’s projection reflects both sides of that equation.
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Global projections and U.S. estimates are different measures
The IEA’s figures describe global use. Separately, the U.S. Department of Energy (DOE) and Lawrence Berkeley National Laboratory reported a 14% increase in U.S. data-center electricity use from 2023 to 2024 in their 2025 update. That U.S.-only change should not be treated as the global growth rate.
How AI is changing server density and cooling
AI workloads can concentrate substantial computing capacity in a smaller number of racks, increasing the heat that cooling systems must remove from particular areas. Rack-density figures in industry guidance illustrate specific high-performance contexts; they are not typical-facility averages or a single threshold that applies to every site.
ASHRAE’s AI Data Center Energy Performance Framework says: “For purpose-built AI data centers where compute densities routinely exceed 50–120 kW per rack and have the potential to trend higher, utilize a TCS.” Here, TCS means a technology cooling system. The Federal Energy Management Program’s 2024 DOE guide separately describes high-performance computing deployments above 125 kW per compute rack. The figures come from different guidance contexts and should not be combined into a universal trigger point.
Air and liquid cooling can coexist
Direct liquid cooling approaches include cold plates and immersion. Liquid cooling is gaining attention, but available evidence does not indicate that it has become standard across the industry. In Uptime Institute’s May 2024 cooling survey, 22% of respondents reported some direct liquid cooling use; among respondents not using it, 61% said they would consider it. These are survey responses, not a census, and reported use can cover only a subset of an organization’s racks.
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Choosing between air cooling, liquid cooling or a combination depends on the workload and facility. Relevant considerations include rack density, server compatibility, cooling capacity and operating conditions, heat rejection, water availability, reliability and maintenance, retrofit needs, potential heat reuse, and capital and operating costs. The evidence supports evaluating those factors for the specific site—not ranking one cooling method as universally superior.
Efficiency means more than one metric
Facility efficiency depends on interactions between IT equipment and the systems supporting it. The DOE’s July 2024 Best Practices Guide for Energy-Efficient Data Center Design covers IT-system efficiency and environmental conditions, air management, cooling and electrical systems, heat recovery, and benchmarking. It notes that IT-level measures can affect downstream mechanical and electrical savings.
The guide cautions against expecting a single best design: “No design guide can offer ‘the most energy-efficient’ data center design, but these guidelines can provide efficiency benefits for a wide variety of data center scenarios.” The right measures depend on the facility’s workload, operating conditions and constraints.
Use energy, water and carbon measures together
ASHRAE recommends tracking multiple dimensions of performance, including Power Usage Effectiveness (PUE), Water Usage Effectiveness (WUE), Water Usage Impact (WUI) and Carbon Usage Effectiveness (CUE). They address different dimensions and are not interchangeable. A facility-level efficiency ratio alone does not show total resource use or how much computing work the facility delivers; comparisons need a clear metric boundary and workload context.
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Power access is becoming a planning constraint
Data centers can be built in a few years, while planning and building energy infrastructure takes longer, the IEA notes. That timing mismatch can constrain how quickly new capacity can be energized. Uptime Institute’s 2025 Global Data Center Survey also identifies power constraints and difficulty forecasting future capacity requirements among industry challenges; those are reported survey findings, not conditions shared by every operator or region.
Power planning involves more than a grid connection. Reliability requirements shape the role of uninterruptible power supply (UPS) batteries and backup generators, which the IEA describes as necessary for reliability but rarely used in ordinary operation. The DOE’s 2025 announcement also discusses onsite generation and storage as options for managing demand and potentially supporting grid flexibility. Whether these approaches fit depends on local grid capacity, reliability needs and project economics.
How to assess a data-center design
There is no universal design choice for every workload or location. For a proposed facility, expansion or retrofit, compare the options against the operating requirements and local constraints:
- Workload and density: What equipment will run, and how concentrated is the compute load across racks?
- Thermal design: Can the cooling system handle the load at required operating conditions, and how will it reject heat?
- Whole-system efficiency: How do IT, air management, cooling and electrical choices affect energy use together?
- Water and carbon: What do the relevant water and carbon measures show alongside energy performance?
- Reliability and operations: What backup capacity, maintenance access and operating procedures are required?
- Site and schedule: Is grid capacity available when needed, and would onsite generation or storage be suitable?
- Lifecycle fit: What are the retrofit requirements, potential for heat reuse, and capital and operating costs?
For operators, the central challenge is coordinating compute growth with cooling, power, water and reliability requirements. AI is accelerating some of those pressures, but decisions still turn on the specific workload and site.
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