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arXiv:2304.03271cs.LG

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

Pengfei Li, Jianyi Yang, Mohammad A. Islam, Shaolei Ren

Abstract

The growing carbon footprint of artificial intelligence (AI) has been undergoing public scrutiny. Nonetheless, the equally important water (withdrawal and consumption) footprint of AI has largely remained under the radar. For example, training the GPT-3 language model 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 is projected to account 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 concerning, as freshwater scarcity has become one of the most pressing challenges. To respond to the global water challenges, AI can, and also must, take social responsibility and lead by example by addressing its own water footprint. In this paper, we provide a principled methodology to estimate the water footprint of AI, and also discuss the unique spatial-temporal diversities of AI's runtime water efficiency. Finally, we highlight the necessity of holistically addressing water footprint along with carbon footprint to enable truly sustainable AI.

arXiv comments
Accepted by Communications of the ACM. Source codes available at: https://github.com/Ren-Research/Making-AI-Less-Thirsty
Journal reference
DOI
Subjects
cs.LGMachine Learning cs.AIArtificial Intelligence

On Hacker News

Linked by 8 identifiable commenters in 10 comments across 7 threads, from Jun 2023 to Jun 2026. Found in 5 HN submissions from Mar 2024 to Jun 2025. Open submission discussions.

Comments linking this paper, per year.

Discussed in comments7 threads

  1. Amazon Says Its Data Centers Use 2.5B Gallons of Water 1 comment

    • According to this study[1] "With good water quality, roughly 80% of water withdrawal is evaporated and considered “consumption"" with the rest being discharged to wastewater facilities. [1] https://arxiv.org/pdf/2304.03271

      GuthwineView on HNParent context

  2. AI uses less water than the public thinks 1 comment

    • Your source cites https://www.washingtonpost.com/technology/2024/09/18/energy-... which in turn claims to be based on https://arxiv.org/abs/2304.03271 but uses 0.14 kWh as the energy consumption for a 100-token request to GPT-4, which is an order of magnitude larger than any…

      yorwbaView on HNParent context

  3. AGI fantasy is a blocker to actual engineering 1 comment

    • I went down that “water use” rabbit hole a month ago and basically… it’s just a bunch of circular reporting that was based on some paper published in 2023[1]. For ChatGPT 3.5 they claimed “500ml for 10-50 responses”. In 2024, Washington Post published an article that took their…

      cruffle_duffleView on HNParent context

  4. Bored of It 1 comment

    • Average phone allegedly uses 30 kWh per year (charging + data transfer)[0], which in turn uses about 26 gallons of water[1]. Which I feel is well worth it for the value each user gets out, but does start to add up to lakes and power plants worth of water/energy when there are…

      UkvView on HNParent context

  5. Using ChatGPT is not bad for the environment 1 comment

    • I published this as a comment as well, but it's probably worth nothing that the ChatGPT water/power numbers cited (the one that is most widely cited in these discussions) comes from an April 2023 paper (Li et al, arXiv:2304.03271) that estimates water/power usage based off of…

      lhlView on HNParent context

  6. A bottle of water per email: the hidden environmental costs of using AI chatbots 4 comments

    • You could argue that this is no longer the case once the model is done; the cost per request will go down over time, as the set amount of power and coolant pumped through data centres gets divided over more people. However, AI companies can't afford to stand still. They have to…

      jeroenhdView on HNParent context

    • Link to the paper in question: https://arxiv.org/pdf/2304.03271 I don’t know why the URL was replaced with this much crappier Washington post article.

      gcrView on HNParent context

    • Your comment is addressed in the introduction section of the paper being discussed. Do give it a read. https://arxiv.org/pdf/2304.03271

      gcrView on HNParent context

    • According to the paper (https://arxiv.org/pdf/2304.03271), the vast majority of water usage is not from cooling but indirectly from energy usage. The paper in turn gets its electricity water intensity from: https://www.wsp.com/-/media/insights/us/documents/wp_guidanc...…

      UkvView on HNParent context

  7. ChatGPT requires half a liter of water to have a conversation 1 comment

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