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 may 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，特别是如GPT-3等大型模型的碳足迹正受到公众关注。然而，同样重要且规模巨大的AI模型水足迹（取水与耗水）却未得到足够重视。例如，在微软最先进的美国数据中心训练GPT-3可直接蒸发70万升清洁淡水，但此类信息一直保密。更严峻的是，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。这令人高度担忧，因为在人口快速增长、水资源枯竭及水利基础设施老化的背景下，淡水稀缺已成为全人类最紧迫的挑战之一。为应对全球水资源挑战，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。本文提出基于原则的方法论来估算AI模型的水足迹，并探讨AI模型运行时水资源效率的独特时空差异性。最后，我们强调需要将水足迹与碳足迹进行系统性整合，以实现真正的可持续AI。