mlsanityData to 7 Oct 2026

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

arXiv 2304.03271 ↗First posted 6 Apr 2023cs.LG cs.AIcited by 44

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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.

Key facts. Cited by 44 papers in the map (data through 7 Oct 2026). Builds on 19 of its 93 references that are in the map. First posted on arXiv 6 Apr 2023.

Builds on

19 of 93 references.

References · 19

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  3. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,242
  4. 2022LaMDA: Language Models for Dialog Applicationscited by 1,218
  5. 2023Capabilities of GPT-4 on Medical Challenge Problemscited by 630
  6. 2019Green AIcited by 620
  7. 2021Carbon Emissions and Large Neural Network Trainingcited by 561
  8. 2022DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scalecited by 400
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  10. 2022Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Modelcited by 264
  11. 2020Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learningcited by 246
  12. 2021Sustainable AI: Environmental Implications, Challenges and Opportunitiescited by 242
7 more
  1. 2022The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrinkcited by 183
  2. 2019Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloadscited by 79
  3. 2022Carbon Explorer: A Holistic Approach for Designing Carbon Aware Datacenterscited by 31
  4. 2020Accelerator-aware Neural Network Design using AutoMLcited by 30
  5. 2020AutoDNNchip: An Automated DNN Chip Predictor and Builder for Both FPGAs and ASICscited by 26
  6. 2021Enabling Sustainable Clouds: The Case for Virtualizing the Energy Systemcited by 12
  7. 2023The Carbon Emissions of Writing and Illustrating Are Lower for AI than for Humanscited by 10

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Citing papers per year: 2023: 6; 2024: 16; 2025: 10; 2026: 12.

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  1. 2023A Survey of Techniques for Optimizing Transformer Inferencecited by 35
  2. 2023Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectivescited by 31
  3. 2025Holistically Evaluating the Environmental Impact of Creating Language Modelscited by 26
  4. 2024A Survey of Sustainability in Large Language Models: Applications, Economics, and Challengescited by 18
  5. 2025A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learningcited by 15
  6. 2024The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resourcescited by 11
  7. 2023Towards Environmentally Equitable AI via Geographical Load Balancingcited by 10
  8. 2024Large Language Models for Human-Machine Collaborative Particle Accelerator Tuning through Natural Languagecited by 9
  9. 2024Sociotechnical Implications of Generative Artificial Intelligence for Information Accesscited by 6
  10. 2024The Impact of Quantization and Pruning on Deep Reinforcement Learning Modelscited by 6
  11. 2023AI Regulation in Europe: From the AI Act to Future Regulatory Challengescited by 5
  12. 2024AI, Climate, and Transparency: Operationalizing and Improving the AI Actcited by 4
32 more
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  2. 2024Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunitiescited by 3
  3. 2024What Generative Artificial Intelligence Means for Terminological Definitionscited by 2
  4. 2024Can LLMs substitute SQL? Comparing Resource Utilization of Querying LLMs versus Traditional Relational Databasescited by 2
  5. 2025Limits to AI Growth: The Ecological and Social Consequences of Scalingcited by 2
  6. 2025Analyzing the Resource Utilization of Lambda Functions on Mobile Devices: Case Studies on Kotlin and Swiftcited by 2
  7. 2025From FLOPs to Footprints: The Resource Cost of Artificial Intelligencecited by 2
  8. 2023Are You Worthy of My Trust?: A Socioethical Perspective on the Impacts of Trustworthy AI Systems on the Environment and Human Societycited by 1
  9. 2024Artificial intelligence and the transformation of higher education institutionscited by 1
  10. 2024Double Jeopardy and Climate Impact in the Use of Large Language Models: Socio-economic Disparities and Reduced Utility for Non-English Speakerscited by 1
  11. 2025The Unbearable Lightness of Prompting: A Critical Reflection on the Environmental Impact of genAI use in Design Educationcited by 1
  12. 2025On the (im)possibility of sustainable artificial intelligence. Why it does not make sense to move faster when heading the wrong waycited by 1
  13. 2026How Hyper-Datafication Impacts the Sustainability Costs in Frontier AIcited by 1
  14. 2024Why Algorithms Remain Unjust: Power Structures Surrounding Algorithmic Activitycited by 0
  15. 2024Building Socially-Equitable Public Modelscited by 0
  16. 2024Towards Socially and Environmentally Responsible AIcited by 0
  17. 2024Integrating AI's Carbon Footprint into Risk Management Frameworks: Strategies and Tools for Sustainable Compliance in Banking Sectorcited by 0
  18. 2024Towards Environmentally Equitable AIcited by 0
  19. 2025Recent Advances, Applications and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2024 Symposiumcited by 0
  20. 2025Systematic Weight Evaluation for Pruning Large Language Models: Enhancing Performance and Sustainabilitycited by 0
  21. 2025Local Differences, Global Lessons: Insights from Organisation Policies for International Legislationcited by 0
  22. 2026Concentrated siting of AI data centers drives regional power-system stress under rising global compute demandcited by 0
  23. 2026Environmental Footprint of GenAI Research: Insights from the Moshi Foundation Modelcited by 0
  24. 2026Sustainable Metal-Organic Framework Water Harvesters in the Artificial Intelligence Eracited by 0
  25. 2026Evaluation of ML Resource Utilization Requires Model Life Cycle Assessmentcited by 0
  26. 2026Using AI in engineering education: a balancing act, driven by clear purposecited by 0
  27. 2026Life Cycle Assessment of Pre-training the Lucie 7B Open-Source Large Language Model on the Jean Zay Supercomputercited by 0
  28. 2026Enhancing SLMs for Sustainable Code Optimization in Radio-Astronomycited by 0
  29. 2026User-Assisted Collaborative Distributed Inference for Efficient QoS-Aware Autoscalingcited by 0
  30. 2026InFactPlanner: Planning Sustainable Geo-Distributed LLM Data Centerscited by 0
  31. 2026Stress-Testing Efficient Responsible-AI Evaluation: When Compute Savings Change Benchmark Conclusionscited by 0
  32. 2026Beyond the Last Truffula Tree: SustainAI - A Water-Aware, Closed-Loop Framework for Environmentally Accountable AIcited by 0