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

arXiv: https://arxiv.org/abs/2304.03271 · HTML: https://mlsanity.com/paper/2304.03271/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 93 references that are in the map.
- First posted on arXiv 6 Apr 2023.

## Builds on (most cited first, 19 of 19)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022)
- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Carbon Emissions and Large Neural Network Training](https://mlsanity.com/paper/2104.10350/) (2021)
- [DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale](https://mlsanity.com/paper/2201.05596/) (2022)
- [Tackling Climate Change with Machine Learning](https://mlsanity.com/paper/1906.05433/) (2019)
- [Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model](https://mlsanity.com/paper/2211.02001/) (2022)
- [Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning](https://mlsanity.com/paper/2002.05651/) (2020)
- [Sustainable AI: Environmental Implications, Challenges and Opportunities](https://mlsanity.com/paper/2111.00364/) (2021)
- [The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink](https://mlsanity.com/paper/2204.05149/) (2022)
- [Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads](https://mlsanity.com/paper/1901.05758/) (2019)
- [Carbon Explorer: A Holistic Approach for Designing Carbon Aware Datacenters](https://mlsanity.com/paper/2201.10036/) (2022)
- [Accelerator-aware Neural Network Design using AutoML](https://mlsanity.com/paper/2003.02838/) (2020)
- [AutoDNNchip: An Automated DNN Chip Predictor and Builder for Both FPGAs and ASICs](https://mlsanity.com/paper/2001.03535/) (2020)
- [Enabling Sustainable Clouds: The Case for Virtualizing the Energy System](https://mlsanity.com/paper/2106.08872/) (2021)
- [The Carbon Emissions of Writing and Illustrating Are Lower for AI than for Humans](https://mlsanity.com/paper/2303.06219/) (2023)

## Built on by (most cited first, 30 of 44)

- [A Survey of Techniques for Optimizing Transformer Inference](https://mlsanity.com/paper/2307.07982/) (2023)
- [Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives](https://mlsanity.com/paper/2311.09227/) (2023)
- [Holistically Evaluating the Environmental Impact of Creating Language Models](https://mlsanity.com/paper/2503.05804/) (2025)
- [A Survey of Sustainability in Large Language Models: Applications, Economics, and Challenges](https://mlsanity.com/paper/2412.04782/) (2024)
- [A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning](https://mlsanity.com/paper/2504.21099/) (2025)
- [The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources](https://mlsanity.com/paper/2406.16746/) (2024)
- [Towards Environmentally Equitable AI via Geographical Load Balancing](https://mlsanity.com/paper/2307.05494/) (2023)
- [Large Language Models for Human-Machine Collaborative Particle Accelerator Tuning through Natural Language](https://mlsanity.com/paper/2405.08888/) (2024)
- [AI Regulation in Europe: From the AI Act to Future Regulatory Challenges](https://mlsanity.com/paper/2310.04072/) (2023)
- [Sociotechnical Implications of Generative Artificial Intelligence for Information Access](https://mlsanity.com/paper/2405.11612/) (2024)
- [The Impact of Quantization and Pruning on Deep Reinforcement Learning Models](https://mlsanity.com/paper/2407.04803/) (2024)
- [AI, Climate, and Transparency: Operationalizing and Improving the AI Act](https://mlsanity.com/paper/2409.07471/) (2024)
- [CACTUS: a Comprehensive Abstraction and Classification Tool for Uncovering Structures](https://mlsanity.com/paper/2308.12031/) (2023)
- [Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunities](https://mlsanity.com/paper/2410.08534/) (2024)
- [What Generative Artificial Intelligence Means for Terminological Definitions](https://mlsanity.com/paper/2402.16139/) (2024)
- [Can LLMs substitute SQL? Comparing Resource Utilization of Querying LLMs versus Traditional Relational Databases](https://mlsanity.com/paper/2404.08727/) (2024)
- [Limits to AI Growth: The Ecological and Social Consequences of Scaling](https://mlsanity.com/paper/2501.17980/) (2025)
- [Analyzing the Resource Utilization of Lambda Functions on Mobile Devices: Case Studies on Kotlin and Swift](https://mlsanity.com/paper/2502.07809/) (2025)
- [From FLOPs to Footprints: The Resource Cost of Artificial Intelligence](https://mlsanity.com/paper/2512.04142/) (2025)
- [Are You Worthy of My Trust?: A Socioethical Perspective on the Impacts of Trustworthy AI Systems on the Environment and Human Society](https://mlsanity.com/paper/2309.09450/) (2023)
- [Artificial intelligence and the transformation of higher education institutions](https://mlsanity.com/paper/2402.08143/) (2024)
- [Double Jeopardy and Climate Impact in the Use of Large Language Models: Socio-economic Disparities and Reduced Utility for Non-English Speakers](https://mlsanity.com/paper/2410.10665/) (2024)
- [The Unbearable Lightness of Prompting: A Critical Reflection on the Environmental Impact of genAI use in Design Education](https://mlsanity.com/paper/2501.16061/) (2025)
- [On the (im)possibility of sustainable artificial intelligence. Why it does not make sense to move faster when heading the wrong way](https://mlsanity.com/paper/2503.17702/) (2025)
- [How Hyper-Datafication Impacts the Sustainability Costs in Frontier AI](https://mlsanity.com/paper/2602.00056/) (2026)
- [Stress-Testing Efficient Responsible-AI Evaluation: When Compute Savings Change Benchmark Conclusions](https://mlsanity.com/paper/2608.31108/) (2026)
- [Why Algorithms Remain Unjust: Power Structures Surrounding Algorithmic Activity](https://mlsanity.com/paper/2405.18461/) (2024)
- [Building Socially-Equitable Public Models](https://mlsanity.com/paper/2406.02790/) (2024)
- [Towards Socially and Environmentally Responsible AI](https://mlsanity.com/paper/2407.05176/) (2024)
- [Integrating AI's Carbon Footprint into Risk Management Frameworks: Strategies and Tools for Sustainable Compliance in Banking Sector](https://mlsanity.com/paper/2410.01818/) (2024)

## Abstract (arXiv)

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.