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Short answer: AI data centers consume large amounts of electricity because they run power-hungry servers, especially GPU-accelerated systems, around the clock. They can also consume water directly for cooling and indirectly through the power plants that generate their electricity.
There is no single water cost for an AI query or one authoritative global water total. Workload-level estimates vary by more than 10,000-fold under different conditions, while the boundary used for accounting—on-site cooling alone or cooling plus electricity generation—can change the result substantially.
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What an AI data center is actually running
A data center combines servers, storage, networking equipment and the electrical and mechanical systems that keep them operating. AI services increasingly use accelerated servers built around specialized chips such as graphics processing units (GPUs). These chips perform the parallel calculations used to train models and generate responses, but they draw substantial power and produce heat.
The electricity meter for a facility therefore captures more than computation. Power delivery equipment, uninterruptible-power systems, networking, lighting, pumps, fans and cooling plants all add demand. In the International Energy Agency’s 2025 analysis, servers account for around 60% of electricity demand in modern data centers on average. That is an IEA estimate, not a fixed share that applies to every building; the percentage changes with facility design and workload.
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Training and inference have different patterns
Training repeatedly processes enormous data sets while adjusting a model’s parameters. A large training run can keep thousands of accelerators busy for weeks or months. Once a model is deployed, inference—the calculation that produces each answer, image or prediction—runs whenever users or software make requests. Popular services may operate continuously, so a model that was trained once can create a persistent electricity load through ongoing inference.
Why cooling is part of the electricity bill
Nearly all electricity entering a computing chip eventually becomes heat that must be removed. Fans, pumps, chillers, heat exchangers and control systems consume power while doing that work. A facility with efficient servers and a favorable climate may need less cooling energy than a similar facility in a hot location, even if both perform the same calculations. The resulting total is often described through a facility-efficiency metric, but the underlying drivers remain the workload, equipment and site conditions.
How large is data-center electricity demand?
The figures below come from different reports and years. They should not be treated as one perfectly continuous time series: the IEA’s 2025 and 2026 analyses use different publication dates and data vintages, while the U.S. estimate comes from a separate Department of Energy and Lawrence Berkeley National Laboratory study.
| Scope and source | Electricity figure | How to interpret it |
|---|---|---|
| Global, IEA 2025 | About 415 TWh in 2024 | Estimated global data-center consumption, approximately 1.5% of worldwide electricity use that year |
| Global, IEA 2025 base case | About 945 TWh in 2030 | Projection, not a measured outcome; the IEA notes substantial uncertainty |
| Global, IEA 2026 analysis | Demand increased 17% in 2025 | Year-over-year change reported by the IEA |
| Global, IEA 2026 projections | Data-center demand doubles by 2030; AI-focused data-center use triples | Report projections, not a claim that every increase is caused by AI |
| United States, DOE/LBNL 2026 | 192 TWh in 2024 | Estimated U.S. data-center use, equal to 4.7% of U.S. electricity consumption |
| United States, DOE/LBNL 2030 reference case | 649 TWh | Reference-case projection; compounded uncertainty scenarios range from 521 to 843 TWh |
Conventional cloud services, enterprise software, storage and network activity also consume electricity. Even when AI is the fastest-growing component, it would be inaccurate to assign all data-center growth to AI. The IEA’s global totals describe data centers as a whole, while its AI-focused projection isolates a subset of facilities and workloads.
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Why AI pushes electricity demand upward
Accelerators perform more work per server
AI training and inference use matrix and tensor operations that specialized accelerators can execute in parallel. A single accelerator can draw far more power than a general-purpose processor, and a training cluster may contain many thousands of them. The cluster also needs high-speed networking so that the chips can exchange model data, adding another electrical load.
High utilization makes the load persistent
Training jobs are scheduled to keep expensive hardware busy. Inference capacity must be available when demand arrives, often with spare servers for peaks, maintenance and resilience. A facility can therefore consume significant power even when an individual user sees only a short response time.
More capability means more infrastructure
Larger models and higher-resolution outputs require additional computation, memory and data movement. Operators may refresh servers as new accelerators become available, which changes both the electricity used by active equipment and the embodied resources associated with replacing older hardware. The 2025 Lawrence Berkeley National Laboratory study identifies server efficiency, utilization and refresh cycle as variables that materially affect workload-level water use as well as energy performance.
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The two ways an AI data center uses water
Direct water consumption for cooling
Some data centers use evaporative cooling. Water absorbs heat as it evaporates, allowing the facility to reject heat with less compressor electricity than some fully mechanical systems. The trade-off is that part of the water leaves the site as vapor, and additional water may be discharged or treated to control mineral buildup. The amount depends on the cooling design, weather, operating temperature and local water rules.
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Indirect water consumption through electricity generation
Power plants can consume water for steam cycles, cooling and fuel processing. Electricity bought by a data center therefore carries a water footprint that may occur far from the servers. Microsoft Chief Sustainability Officer Melanie Nakagawa summarized both pathways in an April 2, 2024 company post: “Currently, many datacenters rely on water for two reasons: directly for cooling, and indirectly for electricity generation.”
A water figure that counts only a cooling tower is not comparable with one that also includes water consumed while generating electricity. Analysts should also distinguish withdrawal—water taken from a source—from consumption, the portion not returned to that source in the same condition or timeframe.
How much water does an AI data center use?
The most accurate general answer is that it depends, and a current comparable global total has not been established with a consistent boundary covering both on-site cooling and electricity generation. A 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 result demonstrates how strongly conditions change the estimate; it is not a claim that a typical prompt consumes a particular number of liters.
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What determines the water footprint of a workload?
The LBNL analysis identifies several interacting determinants:
- Server efficiency: More efficient hardware can complete the same computation with less electricity and associated power-sector water.
- Water intensity of electricity: The generation mix and technologies supplying a site can have very different water requirements.
- Server utilization: Low utilization spreads infrastructure and idle energy over fewer useful computations.
- Cooling system: Air, evaporative, liquid and hybrid designs shift the balance between direct water and electricity.
- Infrastructure efficiency: Electrical losses, pumps, fans and cooling controls add overhead beyond the chips.
- Climate zone: Temperature and humidity affect how often mechanical or evaporative cooling is required.
- Inactive-server share: Powered equipment doing little useful work can increase the water and energy attributed to each task.
- Server refresh cycle: Replacing equipment changes efficiency and the timing of resource use.
Because these factors interact, there is no one-size-fits-all recipe for minimizing water. A design that reduces direct water may require more electricity, shifting part of the impact to power generation. Energy and water should therefore be evaluated together rather than optimized as isolated numbers.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Cooling approaches and their trade-offs
Air-cooled systems
Direct air cooling can avoid routine evaporative water use at the server, although fans and chillers may increase electricity demand in hot conditions. Microsoft lists direct air cooling as one way to reduce reliance on freshwater. The result depends on the site climate and the efficiency of the equipment; “air-cooled” does not mean the facility has no water footprint once electricity generation is counted.
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Evaporative and water-cooled systems
Water-cooled designs can remove heat efficiently, particularly where evaporation provides useful cooling. Their direct consumption varies with weather, operating set points, cycles of concentration and the source water used. Reclaimed water can reduce pressure on drinking-water supplies, but it does not make the physical consumption disappear.
Direct-to-chip liquid cooling
Liquid can be delivered close to the hottest components through cold plates or related direct-to-chip systems. Microsoft’s 2025 Environmental Sustainability Report says one such 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, and it does not describe every AI data center.
Water-free designs and alternative sources
In a December 2024 announcement, Microsoft said a new design launched in August 2024 consumes zero water for cooling. The company also says its existing fleet continues to use a mix of air-cooled and water-cooled systems. Microsoft describes rainwater harvesting and reclaimed water as additional ways to reduce reliance on freshwater. These announcements apply to the company’s designs and should not be generalized to all operators or facilities.
How to evaluate an electricity or water claim
- Identify the boundary. Check whether the number covers servers only, the whole facility, on-site cooling water, electricity-generation water or a combination.
- Check the geography and date. A result for one U.S. region or one season cannot automatically represent global operations.
- Separate measured values from projections. The IEA’s 415 TWh global figure for 2024 is an estimate, while 945 TWh for 2030 is a base-case projection with uncertainty.
- Ask what workload was modeled. Training, batch inference and short interactive requests have different utilization and hardware profiles.
- Look for withdrawal versus consumption. Those terms describe different relationships with local water sources and should not be merged.
- Compare water and energy together. A lower direct-water result may coincide with higher electricity use, while a cleaner power supply can change indirect water intensity.
What the numbers mean for communities and planners
Global percentages can make data-center demand appear small, yet a large facility concentrates load in one transmission area and one watershed. Local consequences depend on the site’s climate, available grid capacity, cooling design and water sources. A national estimate such as the U.S. 192 TWh figure cannot reveal whether a particular town faces water stress or whether a project can operate without new generation and transmission.
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Bottom line
AI data centers use so much electricity because accelerated servers perform intensive computation at high utilization, while cooling and other facility systems add overhead. They use water either at the site for heat removal, through the electricity system that powers them, or through both pathways. The responsible answer to any water question is conditional: name the workload, location, cooling method, power source, accounting boundary and date instead of attaching a universal water cost to every AI prompt.
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