A FIELD GUIDE TO INFERENCE COOLING
Large language models run on racks that get hot, and the cheapest way to cool most of them is to evaporate fresh water into the air. This is a plain accounting of how much, drawn from the few numbers companies and researchers have actually published.
ESTIMATED GLOBAL AI COOLING WATER
SINCE YOU OPENED THIS PAGE
A rough illustration, not a live meter — modeled from published per-query estimates and public traffic figures. See methodology below.
direct on-site cooling water only — excludes water used to generate the electricity
Ren et al.'s modeled figure if the same cluster ran in a hotter, more humid region
estimate from Meta's own model card, on-site cooling only
up 17% year over year, tracked against 2022 in its own environmental report
up 34% year over year, the steepest jump the company had disclosed, coinciding with its AI buildout
Neither company breaks out the AI-specific share of these totals — data centers also run search, cloud storage, and productivity software. The jump in growth rate is the clearest signal available of what generative AI added on top.
Most large data centers cool their servers with evaporative towers: warm water is sprayed over a mesh, a fraction turns to vapor and carries the heat away, and the rest recirculates. It's cheap and energy-efficient — far less electricity than running chillers around the clock — but the water that evaporates doesn't come back down the drain. It leaves the local watershed as humidity.
That's the direct, "on-site" water. There's a second, larger pool of "off-site" water used upstream to generate the electricity itself, mostly at thermoelectric power plants that cool turbines the same way. Most public model-level estimates, including the ones on this page, cover on-site cooling only — so they understate the total.