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AI data centers use large amounts of electricity because they run dense, continuously operating servers with specialized accelerators, while cooling and power-delivery equipment add to the facility load. They use water either directly in cooling systems or indirectly when power plants consume water to generate the electricity those servers need.
There is no single, defensible amount of water per AI query. A 2025 Lawrence Berkeley National Laboratory study found more than a 10,000-fold spread in workload-level water use across the conditions it examined. The result depends on the model and workload, server efficiency and utilization, cooling design, climate, electricity source, infrastructure losses, inactive-server share and accounting boundary.
What is consuming the electricity?
An AI data center is a data center built or configured to run machine-learning workloads. It contains servers, networking, storage, power-conversion equipment, cooling systems and monitoring controls. The servers do the computation, but every supporting system affects the facility’s total electricity demand.
Accelerated servers are unusually power-dense
Training and serving modern AI models commonly uses accelerated servers with chips such as graphics processing units (GPUs). These chips perform large numbers of matrix operations in parallel. A training run may keep many accelerators active for days or weeks; an inference service may keep them available around the clock so it can respond quickly to users. The electricity is therefore tied to both the amount of computation and the time the equipment remains powered.
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The International Energy Agency (IEA) estimated in its 2025 analysis that servers account for around 60% of electricity demand in modern data centers on average. That is an IEA estimate, not a fixed share for every facility: an AI site with exceptionally dense accelerators, a lightly loaded enterprise site and a facility with different cooling or networking designs can have very different breakdowns.
Facility systems add to the server load
Electricity also runs pumps, fans, chillers, heat exchangers, power distribution, backup systems, lighting, networking and control equipment. Power is lost when alternating current is converted for servers and when batteries or other backup equipment are charged. Cooling systems remove the heat produced by computation; the hotter and denser the equipment, the more carefully the facility must move that heat away.
For that reason, a server’s nameplate wattage is not the same as a data center’s total demand. Operators measure the whole facility, including the overhead needed to deliver reliable power and maintain safe temperatures.
How large is data-center electricity demand?
Global and national figures answer different questions, and estimates from different report years should not be treated as one continuous series.
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| Measure | Figure | Scope and status |
|---|---|---|
| Global data-center electricity use in 2024 | About 415 TWh, or roughly 1.5% of global electricity use | IEA 2025 estimate for the worldwide data-center sector; historical estimate, not an AI-only total |
| Global data-center electricity use in 2030 | About 945 TWh | IEA 2025 base-case projection; the agency emphasizes substantial uncertainty |
| Change in global data-center electricity demand in 2025 | 17% increase | IEA 2026 analysis; a reported sector change, not a measurement of AI alone |
| Global demand outlook to 2030 | Data-center demand projected to double; AI-focused data-center electricity projected to triple | IEA 2026 report projections, not observed outcomes |
| U.S. data-center electricity use in 2024 | 192 TWh, equal to 4.7% of U.S. electricity consumption | 2026 estimate from the U.S. Department of Energy and Lawrence Berkeley National Laboratory; national, not global |
| U.S. data-center electricity use in 2030 | 649 TWh in the reference case | DOE/LBNL 2026 reference-case projection |
| U.S. 2030 uncertainty range | 521–843 TWh | DOE/LBNL 2026 compounded uncertainty scenarios |
These totals include conventional servers, cloud services, storage and networking as well as AI. It would be incorrect to attribute all growth to AI. AI is, however, a major reason operators are deploying more accelerator-heavy capacity, and the IEA’s 2026 analysis specifically projects faster growth for AI-focused facilities.
Why AI workloads raise power demand
Training concentrates computation
Training adjusts a model’s parameters by processing very large datasets repeatedly. The work is distributed across many accelerators connected by high-speed networking. Running more chips simultaneously can shorten a training schedule, but it also creates a large instantaneous load and concentrates heat in a relatively small room.
Inference runs after training ends
Once a model is trained, inference systems generate answers, classifications, images or other outputs for users and applications. Popular services may serve requests continuously, including at times when individual requests are brief. Keeping accelerators ready, routing traffic and meeting response-time targets can consume substantial energy even though no single request resembles a full training run.
Demand is affected by efficiency and utilization
Newer chips may perform more work per joule, while larger models, longer outputs and higher request volumes can increase total work. A server that is powered on but waiting for work still has a baseline draw. Conversely, high utilization can spread fixed equipment overhead across more useful computation. The balance changes by model, scheduling policy, hardware generation and facility.
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Where does the water use come from?
Water has two distinct pathways. Melanie Nakagawa, Microsoft’s chief sustainability officer, summarized them in an April 2, 2024 company blog post: “Currently, many datacenters rely on water for two reasons: directly for cooling, and indirectly for electricity generation.” Both pathways matter, and omitting one changes the answer.
Direct water use for cooling
Servers convert nearly all the electricity they consume into heat. In evaporative cooling systems, water absorbs heat and some of it evaporates, so the facility must replace that water. Cooling towers, evaporative chillers and related treatment systems may also discharge water to control mineral buildup. The amount depends on local weather, the heat load, the system’s operating settings and the quality and availability of incoming water.
Other facilities use air-cooled equipment, closed-loop liquid systems or combinations of methods. A closed loop can circulate the same fluid repeatedly, but it still requires equipment to reject heat and may have water needs elsewhere in the site. “Water-free” usually describes a particular cooling function or design boundary, not the disappearance of every water-related impact.
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Power plants can consume or withdraw water for cooling and other generation processes. The data center does not use that water on its premises, but its electricity demand contributes to the power-system requirement. The indirect amount varies with the local generation mix, plant technology, weather and transmission region. A site supplied mainly by one type of generation will not have the same water intensity as a site supplied by another.
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How much water does an AI data center use?
The most accurate answer is that no universal per-query or per-facility number has been established. The 2025 Lawrence Berkeley National Laboratory study found more than a 10,000-fold variation in workload-level water use across the cases it assessed. That spread reflects different assumptions and operating conditions; it is not a claim that a typical prompt consumes a particular volume.
Variables that can change the estimate
- Server efficiency: newer or better-configured hardware can complete a workload with less electricity, reducing heat and potentially cooling demand.
- Server utilization: low utilization leaves powered equipment and facility capacity serving fewer completed tasks.
- Electricity-related water intensity: the generation mix and power-plant technologies determine indirect water use.
- Cooling system: evaporative, air, liquid and hybrid designs have different direct-water and electricity requirements.
- Infrastructure efficiency: power conversion, networking and cooling overhead change the energy attributable to a workload.
- Climate zone: outdoor temperature and humidity affect how much heat a site must reject and which cooling modes are practical.
- Inactive-server share: equipment that is available but not processing useful work can raise the water and energy allocated to each completed task.
- Refresh cycle: manufacturing and replacing servers changes the time period and boundary used in a lifecycle calculation.
Because these factors interact, the study found no one-size-fits-all recipe for minimizing water use. A comparison should identify the model, workload, facility location, date, cooling method, electricity source and allocation method before presenting a number.
Consumption is not the same as withdrawal
Water withdrawal is the amount taken from a source. Water consumption is the portion not returned to that source, often because it evaporates or is incorporated into a product. A report that lists withdrawal cannot be compared directly with one that lists consumption. Readers should also check whether a figure covers only on-site cooling or includes water used to generate electricity.
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Why a single global water total is not available
Current evidence does not provide one authoritative, comparable global total for data-center water consumption that consistently includes both on-site cooling and electricity generation. Facilities report different boundaries, locations and metrics. Some count municipal or freshwater use; others include reclaimed water or estimate power-sector water from an electricity mix. Climate conditions and local water stress also determine the practical consequence of the same volume.
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This limitation does not mean water impacts are negligible. It means a number without its boundary can mislead. For example, a facility that avoids evaporative cooling may reduce its direct site consumption while using more electricity, potentially shifting part of the impact to power generation.
Cooling choices and the trade-off with electricity
Air cooling and mechanical refrigeration
Air-based systems can reduce direct water use, especially in climates where outdoor conditions permit efficient heat rejection. Fans and refrigeration equipment consume electricity, however, and hot or humid conditions can increase that demand. Air cooling also becomes more difficult as accelerator racks produce more heat per unit of floor space.
Evaporative cooling
Evaporation can reject heat efficiently and may reduce compressor electricity in suitable climates. Its direct water consumption is the trade-off. The local source, treatment requirements and seasonal water availability determine whether that trade-off is acceptable.
Liquid and direct-to-chip systems
Liquid cooling places a heat-transfer fluid close to high-power chips, allowing dense equipment to move heat more effectively than room air alone. It can be designed as a recirculating loop, with separate equipment rejecting heat outside the server. The total result still depends on pumps, heat exchangers, chillers and the electricity mix.
Reclaimed water and rainwater
Using reclaimed water or captured rainwater can reduce dependence on potable freshwater where local infrastructure supports it. These approaches do not make water use zero, and their benefits depend on treatment energy, storage, rainfall patterns and competing community needs.
What water-saving designs can and cannot show
Microsoft’s December 2024 announcement said a new data-center design launched in August 2024 consumes zero water for cooling. Microsoft also said its existing fleet continues to use a mix of air-cooled and water-cooled systems. The statement is therefore evidence about a company’s particular design and rollout, not proof that all AI data centers are water-free.
Microsoft’s 2025 Environmental Sustainability Report separately reported that a direct-to-chip cooling innovation saves more than 125 million liters of water per facility each year. That is a company-reported design claim, not an independently verified industry-wide result. The figure should be read with the facility type, baseline system and accounting boundary in mind.
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How to evaluate an electricity or water claim
- Identify the scope. Check whether the figure covers one facility, a national sector or the global data-center industry, and whether it is AI-only or all data-center workloads.
- Check the date and status. Label measured or historical estimates separately from projections. The IEA’s 2025 and 2026 analyses use different publication dates and data vintages.
- Read the water boundary. Determine whether the calculation includes direct cooling only, electricity-generation water, or both.
- Distinguish withdrawal from consumption. These are different metrics and should not be combined in one comparison.
- Look for operating conditions. Model, workload, utilization, server generation, climate, cooling design and electricity source can dominate the result.
- Compare energy and water together. A design that lowers on-site water may increase electricity use, while a more efficient compute system may reduce both heat and indirect water demand.
What readers should take away
- AI facilities consume electricity primarily in servers and specialized accelerators, with cooling and other infrastructure adding overhead.
- Water impacts are direct when cooling uses water and indirect when electricity generation consumes water.
- Workload-level water use is highly variable; a universal “water per prompt” figure is not scientifically meaningful without detailed assumptions.
- The IEA’s 2025 estimate was 415 TWh for global data centers in 2024, with a 945 TWh base-case projection for 2030; the IEA’s 2026 analysis adds separate growth projections and should be cited as its own report.
- U.S. figures from DOE and LBNL are national estimates: 192 TWh in 2024 and a 649 TWh reference case for 2030, with a 521–843 TWh uncertainty range.
- Water-free or low-water cooling designs exist, but their claims apply to specified systems and boundaries rather than every AI data center.
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