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

Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models — mentioned by Guthwine

Thu, 11 Jun 2026 15:27:13 GMT

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

Paper and discussion history. A mention is not an endorsement.

Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models — mentioned by yorwba

Fri, 01 May 2026 20:08:18 GMT

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 figure in that paper. Based on a speed of 18 tokens/s https://openrouter.ai/openai/gpt-4/performance the implied power draw is ≈91 kW, about two thirds of a 72-GPU rack https://www.supermicro.com/datasheet/datasheet_SuperCluster_... I somewhat doubt that the model is large enough to require an entire rack's worth of GPU…

Paper and discussion history. A mention is not an endorsement.

Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models — mentioned by cruffle_duffle

Fri, 14 Nov 2025 17:17:15 GMT

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 claim and said “519 milliliters per email”[2] but didn’t source that from the original paper… that “shocking headline” took off and got widely circulated and cited directly, treating the WaPo calculation as if it were the original research finding. Then tech blogs and advocacy sites ran with it even harder, citing each…

Paper and discussion history. A mention is not an endorsement.

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

Sun, 29 Jun 2025 19:26:41 GMT

Uncovering and addressing the secret water footprint of AI models

1 point; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models — mentioned by Ukv

Fri, 04 Apr 2025 14:08:18 GMT

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 billions of phones. [0]: https://www.bryceenergyservices.com/2024/10/03/the-total-ene... [1]: Using same electricity water intensity as used in https://arxiv.org/pdf/2304.03271

Paper and discussion history. A mention is not an endorsement.

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

Tue, 04 Mar 2025 03:10:22 GMT

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

2 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models — mentioned by lhl

Sat, 18 Jan 2025 07:48:22 GMT

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 GPT-3 (175B dense model) numbers published from OpenAI's original GPT-3 2021 paper. From Section 3.3.2 Inference: > As a representative usage scenario for an LLM, we consider a conversation task, which typically includes a CPU-intensive prompt phase that processes the user’s input (a.k.a., prompt) and a memory-intensive…

Paper and discussion history. A mention is not an endorsement.

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

Wed, 15 Jan 2025 00:52:38 GMT

"GPT training in U.S. data centers can evaporate 700k L of freshwater"

3 points; 2 discussion replies on HN. Paper and source history. A submission is not an endorsement.

Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models — mentioned by Ukv

Wed, 18 Sep 2024 14:40:26 GMT

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... (reference [20]) - which notes: > Even though hydropower water withdrawal and consumption intensities are usually orders of magnitude larger than other types and likely to skew overall regional averages, it is important to include hydropower in the factors to show not only the power sector’s dependency on water but also its…

Paper and discussion history. A mention is not an endorsement.

Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models — mentioned by gcr

Wed, 18 Sep 2024 12:51:25 GMT

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

Paper and discussion history. A mention is not an endorsement.

Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models — mentioned by gcr

Wed, 18 Sep 2024 12:47:45 GMT

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.

Paper and discussion history. A mention is not an endorsement.

Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models — mentioned by jeroenhd

Wed, 18 Sep 2024 11:37:10 GMT

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 keep training or they risk being made irrelevant by whatever AI company comes next. Furthermore, a non-significant amount of energy and cooling is being used for generating responses as well. It's plainly obvious when you run even the very modest AI models at home how much power these things take. The paper[1] mentions…

Paper and discussion history. A mention is not an endorsement.

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

Sun, 11 Aug 2024 17:43:59 GMT

Secret Water Footprint of AI Models

1 point; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

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

Mon, 11 Mar 2024 19:44:22 GMT

Uncovering and Addressing the Secret Water Footprint of AI Models

1 point; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models — mentioned by belter

Thu, 29 Jun 2023 23:48:09 GMT

"Uncovering and Addressing the Secret Water Footprint of AI Models" - https://arxiv.org/abs/2304.03271

Paper and discussion history. A mention is not an endorsement.