The AI Boom Could Triple Data Center Water Use by 2030
Now It May Help Break It Apart Rystad Energy is an independent energy consulting services and business intelligence provider offering global databases, strategic advisory and research products for energy companies and suppliers,… The massive data center capacity scale-up underway has focused attention on the scale of water required for these facilities to operate.
Rystad Energy estimates that without water-saving measures in place, global water consumption by data centers could rise to 644 billion liters per year by 2030.
However, active mitigation could reduce demand to 388 billion liters, representing worst- and best-case scenarios as data center capacity ramps up, driven largely by the growing adoption of artificial intelligence (AI) in everyday life.
Forecasting the water impact of data centers is far from straightforward, with consumption varying significantly depending on cooling technology, climate and whether we measure water withdrawn or water actually consumed.
Based on Rystad Energy estimates, data centers consumed 222 billion liters of water directly for cooling in 2025, but that figure could nearly triple to just under 644 billion liters by 2030 in our risked central case.
More water-efficient pathways could bring this down to 543 billion liters in the moderate case or as low as 388 billion liters in an aggressive water-saving scenario.
These figures only capture direct cooling-water consumption, while the less visible water footprint embedded in the electricity supply adds another layer of complexity, Minh Khoi Le, Global Head of Data Center & Hydrogen Research, Rystad Energy Data centers use water primarily to remove the heat generated by computing equipment, but consumption varies significantly depending on the cooling technology and location.
While AI servers generate substantially more heat, the rise of AI does not necessarily mean a proportional increase in water consumption, as rack-level liquid cooling can reduce the heat burden on facility-level systems and enable greater use of dry cooling.
Technologies such as those proposed for NVIDIA’s Vera Rubin platform could therefore help limit water use, although the potential savings remain highly dependent on climate, with dry cooling generally more effective in colder locations.
More than just on-site water consumption, a useful metric to consider is water-use effectiveness (WUE), the industry measure of water used per unit of IT energy, which can also account for the water consumed to generate the electricity used by a data center.
Some cooling technologies can reduce on-site water use but require more electricity.
For example, dry cooling can save around 2.15 liters of water for every kWh of IT load, but requires an additional 0.30–0.74 kWh of electricity.
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