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The growing carbon footprint of artificial intelligence (AI) models,especially large ones such as GPT-3, has been undergoing public scrutiny.Unfortunately, however, the equally important and enormous water (withdrawaland consumption) footprint of AI models has remained under the radar. Forexample, training GPT-3 in Microsoft's state-of-the-art U.S. data centers candirectly evaporate 700,000 liters of clean freshwater, but such information hasbeen kept a secret. More critically, the global AI demand may be accountablefor 4.2 -- 6.6 billion cubic meters of water withdrawal in 2027, which is morethan the total annual water withdrawal of 4 -- 6 Denmark or half of the UnitedKingdom. This is very concerning, as freshwater scarcity has become one of themost pressing challenges shared by all of us in the wake of the rapidly growingpopulation, depleting water resources, and aging water infrastructures. Torespond to the global water challenges, AI models can, and also must, takesocial responsibility and lead by example by addressing their own waterfootprint. In this paper, we provide a principled methodology to estimate thewater footprint of AI models, and also discuss the unique spatial-temporaldiversities of AI models' runtime water efficiency. Finally, we highlight thenecessity of holistically addressing water footprint along with carbonfootprint to enable truly sustainable AI.

Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models