The growing carbon footprint of artificial intelligence (AI) models, especially large ones such as GPT-3 and GPT-4, has been undergoing public scrutiny. Unfortunately, however, the equally important and enormous water 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 consume 700,000 liters of clean freshwater (enough for producing 370 BMW cars or 320 Tesla electric vehicles) and the water consumption would have been tripled if training were done in Microsoft's Asian data centers, but such information has been kept as a secret. This is extremely 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 should, take social responsibility and lead by example by addressing their own water footprint. In this paper, we provide a principled methodology to estimate fine-grained 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（尤其是GPT-3和GPT-4等大型模型）日益增长的碳足迹已受到公众关注。然而，同样重要且巨大的AI模型水足迹却仍被忽视。例如，在微软最先进的美国数据中心训练GPT-3会直接消耗70万升清洁淡水（足以生产370辆宝马车或320辆特斯拉电动车），而如果在微软的亚洲数据中心进行训练，耗水量将增加两倍，但此类信息一直处于保密状态。这一问题极为令人担忧，因为随着人口快速增长、水资源日益枯竭以及供水基础设施老化，淡水短缺已成为我们共同面临的最紧迫挑战之一。为应对全球水危机，AI模型能够且应当承担社会责任，率先通过解决自身水足迹来树立榜样。本文提出了一项系统化方法论，用于估算AI模型的细粒度水足迹，并探讨了AI模型运行时水效率的独特时空差异性。最后，我们强调了在实现真正可持续AI的过程中，必须将水足迹与碳足迹进行整体性综合考量。