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 (withdrawal and consumption) footprint of AI models has remained under the radar. For example, training GPT-3 in Microsoft's state-of-the-art U.S. data centers can directly evaporate 700,000 liters of clean freshwater, but such information has been kept a secret. More critically, the global AI demand might be accountable for 4.2 -- 6.6 billion cubic meters of water withdrawal in 2027, which is more than the total annual water withdrawal of 4 -- 6 Denmark or half of the United Kingdom. This is very concerning, as freshwater scarcity has become one of the most pressing challenges shared by all of us in the wake of the rapidly growing population, depleting water resources, and aging water infrastructures. To respond to the global water challenges, AI models can, and also must, take social responsibility and lead by example by addressing their own water footprint. In this paper, we provide a principled methodology to estimate the water footprint of AI models, and also discuss the unique spatial-temporal diversities of AI models' runtime water efficiency. Finally, we highlight the necessity of holistically addressing water footprint along with carbon footprint to enable truly sustainable AI.

翻译：人工智能（AI）模型，特别是GPT-3等大型模型，其日益增长的碳足迹正受到公众审视。然而，同样重要且庞大的AI模型水足迹（包括取水和耗水）却鲜为人知。例如，在微软位于美国最先进的数据中心训练GPT-3，可直接蒸发70万升清洁淡水，但此类信息一直被保密。更关键的是，到2027年，全球AI需求可能导致42亿至66亿立方米的取水量，超过4至6个丹麦或半个英国的年度总取水量。这令人深感担忧，因为在人口快速增长、水资源枯竭和供水基础设施老化背景下，淡水短缺已成为我们所有人共同面临的最紧迫挑战之一。为应对全球水挑战，AI模型能够且必须承担社会责任，以身作则解决自身水足迹问题。本文提出了一套评估AI模型水足迹的原则性方法论，并探讨了AI模型运行时水效率独特的时空差异性。最后，我们强调需将水足迹与碳足迹进行整体考量，以实现真正可持续的AI。