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

• URL: http://arxiv.org/abs/2304.03271v3
• Date: Sun, 29 Oct 2023 17:30:08 GMT
• Title: Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models
• Authors: Pengfei Li and Jianyi Yang and Mohammad A. Islam and Shaolei Ren
• Abstract summary: Training GPT-3 in Microsoft's state-of-the-art U.S. data centers can directly evaporate 700,000 liters of clean freshwater. The global AI demand may be accountable for 4.2 -- 6.6 billion cubic meters of water withdrawal in 2027. To respond to the global water challenges, AI models can, and also must, take social responsibility and lead by example.
• Score: 34.93600962447119
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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