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AI data centers manage three coupled constraints: electricity supply, heat removal and water availability. Accelerators consume large amounts of power and convert almost all of it into heat, so operators must secure reliable electricity, remove heat at high rack densities and limit water stress without creating unacceptable energy or reliability penalties. The best design depends on workload, climate, grid mix, cooling architecture and whether water figures include only the facility or also water consumed to generate its electricity.
How much electricity do AI data centers use?
AI workloads are increasing data-center electricity demand faster than traditional enterprise computing because large training and inference clusters pack many accelerators into each rack. The International Energy Agency (IEA) estimated that data centers consumed 460 terawatt-hours (TWh) of electricity worldwide in 2024. In its 2025 Base Case, the IEA projects global data-center electricity generation to exceed 1,000 TWh in 2030 and reach 1,300 TWh in 2035.
The United States shows the same trend at national scale. A 2024 U.S. Department of Energy (DOE) and Lawrence Berkeley National Laboratory (LBNL) report estimated that data centers used 176 TWh in 2023, up from 58 TWh in 2014. That represented 4.4% of total U.S. electricity use in 2023. The report projects 325–580 TWh in 2028, equivalent to 6.7–12% of U.S. electricity consumption depending on how quickly facilities are built and how efficiently hardware and software operate.
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| Measure | Value | Source and qualification |
|---|---|---|
| Global data-center electricity, 2024 | 460 TWh | IEA, 2025 estimate |
| Global data-center electricity, 2030 | More than 1,000 TWh | IEA, 2025 Base Case projection |
| Global data-center electricity, 2035 | 1,300 TWh | IEA, 2025 Base Case projection |
| U.S. data-center electricity, 2014 | 58 TWh | DOE/LBNL, 2024 report |
| U.S. data-center electricity, 2023 | 176 TWh | DOE/LBNL, 2024 report |
| U.S. share of electricity, 2023 | 4.4% | DOE/LBNL, 2024 report |
| U.S. projected electricity, 2028 | 325–580 TWh | DOE/LBNL, 2024 projection |
| U.S. projected electricity share, 2028 | 6.7–12% | DOE/LBNL, 2024 projection |
Near-term supply is not carbon-free by default. The IEA expects renewables to meet nearly half of additional data-center electricity demand through 2030, while natural gas and coal together meet more than 40% of that additional demand. Later growth in renewables and nuclear generation can change the mix, but a new facility’s actual emissions depend on the regional grid and the contracts or generation assets serving it.
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How operators secure reliable power
A data center cannot treat electricity as an ordinary utility bill. Training runs and inference services need continuous power, while a sudden interruption can damage hardware, interrupt customer workloads or require expensive recovery. Operators therefore combine several layers rather than relying on one technology.
Grid interconnection and capacity planning
Developers reserve transmission and distribution capacity years before a campus is full. The interconnection study determines how much firm power is available, what substation and transmission upgrades are needed and whether phased construction is possible. AI clusters can add large blocks of load quickly, so the electrical design must account for future rack density rather than only the first deployment.
Onsite generation and storage
Onsite generation can supply power during grid constraints or outages, and batteries can bridge short interruptions, shave peaks and provide fast response. The DOE has identified onsite generation and storage as ways data centers could become a grid asset instead of only a source of demand. Storage can also shift charging to periods when renewable output is high or wholesale electricity is less carbon-intensive.
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Renewable procurement and flexibility
Operators use power-purchase agreements, utility green tariffs and other contracts to support wind and solar projects. These arrangements do not necessarily mean every hour of consumption is renewable: a facility may still draw grid electricity when contracted projects are not generating. Hourly matching, curtailment management and flexible workloads provide a more precise picture than annual renewable certificates alone.
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“The United States has seen an incredible investment in artificial intelligence and other breakthrough technologies over the last decade and a half, and this industrial renaissance has created greater demand on our domestic energy supply.” — Jennifer M. Granholm, U.S. Energy Secretary
Why AI racks make cooling harder
Almost all electricity entering an accelerator becomes heat. AI systems concentrate many high-power chips in a small footprint, raising heat flux and rack power density beyond what conventional room air systems were designed to handle. Cooling must remove that heat continuously while maintaining chip temperatures, avoiding condensation and allowing technicians to service equipment safely.
LBNL’s 2024 U.S. data-center analysis models nine cooling-system families, including direct-expansion systems, air-cooled chillers, water-cooled chillers, airside and waterside economizers, dry or adiabatic cooling and liquid-cooling options. No single family is best for every site. Engineers compare heat-removal capacity at the target rack density, electrical overhead, onsite water use, climate, reclaimed-water availability, reliability and retrofit complexity.
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Air-cooled chillers reject heat without an evaporative cooling tower, making them useful where onsite freshwater consumption is a primary constraint. Their electricity use can rise in hot weather, and the fans, compressors and pumps add overhead that appears in the facility’s PUE.
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Water-cooled chillers and evaporative systems
Water-cooled chillers can deliver high efficiency and capacity for large cooling loads, particularly when a cooling tower can operate effectively. Evaporation consumes water, however, and drought, water rights or municipal restrictions may limit this approach. Water treatment is also required to control scale, corrosion and biological growth.
Economizers and dry or adiabatic cooling
Airside economizers use suitable outdoor air directly, while waterside economizers use favorable outdoor conditions to reduce compressor operation. Their benefit depends on climate, humidity and filtration requirements. Dry coolers avoid routine evaporation but may require more electricity or larger heat-rejection equipment. Adiabatic systems use water only during selected hot conditions, trading lower peak electricity demand for intermittent water consumption.
Direct-to-chip liquid cooling
Liquid blocks or cold plates sit close to the accelerator, where heat is concentrated. A facility loop carries heat away to a heat exchanger or external radiator. This approach supports high rack densities and can reduce the amount of room air that must circulate. It also introduces liquid-distribution hardware, connector standards, leak detection and maintenance procedures that an air-only retrofit does not need.
How much water does AI cooling use?
Water use varies widely because designs differ in climate, cooling method, operating temperature and water source. Evaporative towers consume water as vapor leaves the system; blowdown water is discharged to control mineral concentration. Air-cooled and closed-loop liquid systems can greatly reduce onsite consumption, but they may require more electricity, especially during hot periods.
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Microsoft’s next-generation design, introduced beginning in August 2024, circulates water in a closed loop at the chip level. Microsoft says the design avoids more than 125 million liters of cooling water per facility per year. The company also notes that replacing evaporative cooling with mechanical cooling can increase PUE; warmer operating temperatures and efficient economizer chillers can mitigate that increase.
“These new liquid cooling technologies recycle water through a closed loop.” — Microsoft
Water source matters as much as volume. Facilities may use potable water, reclaimed municipal wastewater, rainwater or other non-potable supplies where local rules and treatment systems permit. Rainwater harvesting and recycled-water systems reduce demand on drinking-water supplies, but they require storage, treatment and dependable backup sources.
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PUE: electricity overhead
Power Usage Effectiveness (PUE) is calculated as:
PUE = total facility energy ÷ IT-equipment energy
IT energy covers servers, accelerators, storage and networking. Total facility energy also includes cooling, pumps, fans, power conversion, lighting and other infrastructure. A value closer to 1 means less overhead per unit of computing energy. PUE does not reveal the carbon intensity of the electricity or the amount of water used.
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WUE: cooling and humidification water
Water Usage Effectiveness (WUE) is calculated as:
WUE = annual liters of water used for cooling and humidification ÷ annual IT kilowatt-hours
LBNL distinguishes WUE(site), which mainly counts onsite cooling water, from WUE(source), which includes water consumed while generating the electricity used by the facility. A low site WUE can coexist with a higher source WUE if the electricity supply comes from a water-intensive power plant.
| Metric | What it measures | What it does not show by itself |
|---|---|---|
| PUE | Total facility energy relative to IT energy | Carbon intensity, water consumption or workload efficiency |
| WUE(site) | Onsite cooling and humidification water per IT kWh | Water consumed to generate electricity |
| WUE(source) | Water associated with electricity generation per IT kWh | Only the facility’s direct water demand |
Microsoft reported a global PUE of 1.17 and WUE of 0.27 liters per kWh for fully owned and controlled facilities operating for 12 months in fiscal year 2025 (July 1, 2024–June 30, 2025). That figure should not be compared with another operator’s number unless the reporting year, facility boundary, climate, operating maturity and withdrawal-versus-consumption definition match.
How operators balance energy, cooling and water
- Define the workload. Estimate accelerator type, utilization, training duration, inference traffic and future rack density. A lightly used cluster and a continuously saturated training fleet have different cooling and power profiles.
- Screen the site. Check grid capacity, electricity prices, outage risk, ambient temperature, humidity, water availability, drought rules and access to reclaimed water.
- Set reliability targets. Choose redundancy for utility feeds, generators, batteries, pumps, chillers and liquid loops according to the service-level requirement.
- Model cooling alternatives. Compare air, evaporative, economizer, dry, adiabatic and direct-to-chip designs at the intended rack density. Include compressor, fan, pump and water-treatment energy in the PUE model.
- Account for water boundaries. Report onsite withdrawals and consumption separately where possible, identify potable versus reclaimed sources and calculate source-water effects when electricity generation is material.
- Plan for change. Reserve electrical and mechanical capacity for denser accelerators, provide pathways for liquid-cooling distribution and design maintenance procedures before deployment.
- Operate continuously. Tune set points, economizer hours, chiller sequencing, workload schedules and battery dispatch using measured temperatures, flow, energy and water data.
What major operators are doing
Google’s efficiency and carbon-free energy work
Google reported a 27% increase in data-center electricity demand in its 2025 report covering 2024. It said hourly carbon-free energy rose from 64% to 66% and that its data centers used 84% less overhead energy than the industry average. The company also describes model and hardware efficiency measures, including quantization and newer tensor processing units, which reduce the energy required for a given amount of computation.
Microsoft’s water and cooling transition
Microsoft reports using free-air cooling, rainwater harvesting and reclaimed or recycled water, while transitioning toward designs intended to eliminate evaporative water loss. Its operating fleet still combines air- and water-cooled systems, so performance differs by facility. New closed-loop, chip-level designs target water reduction while acknowledging that mechanical cooling can change electricity overhead.
Quick Recap
Common mistakes when comparing data centers
- Comparing PUE without the boundary: One operator may include a full campus while another reports only a building or a partial year.
- Treating WUE as a universal water score: Verify whether the number is WUE(site) or WUE(source), and whether it represents withdrawal or consumption.
- Assuming renewable contracts equal hourly renewable operation: Annual procurement can coexist with fossil-fueled grid electricity at other hours.
- Ignoring climate: A cooling design that performs efficiently in a cool, dry region may consume more electricity or water in a hot, humid one.
- Forgetting workload efficiency: Quantization, better models and newer accelerators can reduce energy per result even while total demand rises.
- Overlooking retrofit limits: Direct-to-chip liquid cooling may require manifolds, heat exchangers, leak detection and service processes that an air-cooled hall lacks.
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