AIプログラムは希少な水を大量に消費する(AI programs consume large volumes of scarce water)


2023-04-28 カリフォルニア大学リバーサイド校(UCR)

water loss from AI
Data processing centers consume water by using electricity from steam generating power plants and by using on-site chillers to keep their servers cool. Graphic image by Evan Fields/UCR



AIをより “渇き “のないものにする: AIモデルの秘密のウォーターフットプリントを発見して対処する
Making AI Less “Thirsty”: Uncovering and Addressing the Secret Water Footprint of AI Models

Pengfei Li, Jianyi Yang, Mohammad A. Islam, Shaolei Ren
arXiv  Submitted on: 6 Apr 2023

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.