AI’s Water Footprint: The Sustainability Cost of Large Language Models

Artificial Intelligence (AI) is expanding rapidly across industries, supported by Large Language Models (LLMs) such as GPT-4, Claude, and Gemini. These models require extensive computational power, both during training and while in regular use. The growing reliance on such systems has raised significant concerns about their environmental impact.

Much attention has been given to AI’s energy consumption and carbon emissions. However, the discussion often overlooks its water usage. Large amounts of water are used to cool data centers. Water is also consumed indirectly in the production of power and computing hardware.

The rising global demand for AI services increases the pressure on already limited freshwater resources. This trend poses sustainability challenges, particularly in areas experiencing water stress and climate-related risks. A clear understanding of AI’s water footprint is necessary. It supports informed decisions for responsible development and long-term environmental planning.

How AI Models Consume Water

Running large-scale AI systems requires nonstop computation in data centers that handle billions of operations. This process generates a significant amount of heat. To prevent hardware failure and maintain optimal performance, heat must be effectively removed. Most data centers use evaporative cooling systems for this purpose. These systems depend heavily on freshwater. A large portion of the water evaporates during cooling and cannot be reused. As a result, the process leads to high levels of water withdrawal and consumption.

Researchers have recently started measuring the water impact of AI training. A 2023 study by teams at UC Riverside and UT Arlington estimated that training a single large model consumed more than 700,000 liters of clean water. That is about the amount needed to produce 370 BMW cars. This shows how much water is used during the early development stages of advanced AI.

Water use continues even after training is complete. Inference, the process of responding to user prompts, also runs on robust computing systems. These systems operate round the clock in many different parts of the world. Every single user request adds to the computing workload. It also increases the cooling demands. The total water used for inference continues to grow due to the widespread adoption of AI tools, such as virtual assistants, chatbots, and search engines.

Worldwide, data centers are estimated to consume more than 560 billion liters of water annually, primarily for cooling. This number is expected to increase sharply by 2030. A significant reason is the rising demand for AI-driven services. In addition to direct use, AI also causes indirect water consumption. This occurs during electricity production, particularly in regions that rely on coal or nuclear power. These energy sources require significant amounts of water for their operations.

This increasing demand for water highlights a serious concern. There is now an urgent need for better cooling systems, sustainable infrastructure, and transparent reporting on water usage. Without action, the continued spread of AI could put even more pressure on freshwater supplies. This is particularly risky for places already facing drought or climate-related stress.

Infrastructure and Cooling Technologies

AI models operate on high-performance chips installed in cloud data centers. These centers require specialized cooling systems to manage the heat produced by continuous computation. The most widely used method is evaporative cooling, in which water is sprayed into the air or across surfaces to absorb heat. A significant portion of this water evaporates and cannot be reused, resulting in high water withdrawal rates.

To address this issue, some data centers are adopting alternative cooling methods such as liquid immersion cooling and direct-to-chip cooling. These techniques use thermally conductive fluids or closed-loop coolant systems to remove heat from processors. Although more efficient, they still involve indirect water usage. This occurs during the system setup or through electricity generation, particularly in regions where power is produced from coal or nuclear sources, both of which require large amounts of water for steam production and cooling.

Cooling strategies also vary depending on climate and location. In areas facing water scarcity, data center operators are shifting away from evaporative cooling and instead using air-based or closed-loop systems to reduce water consumption. However, these alternatives often demand more energy, creating a trade-off between water savings and carbon emissions.

Every component of AI infrastructure, from chip-level heat removal to full-facility cooling and electricity generation, adds to the overall water footprint. The growing demand for AI necessitates improvements to cooling and power systems. Without better efficiency, the pressure on water resources will continue to rise.

Geographic and Environmental Influences on Data Centre Water Consumption

The water consumption of data centers is strongly influenced by their geographic location and local environmental conditions. In areas with high temperatures, such as Arizona or Texas, cooling systems must work harder to keep servers at a stable operating temperature. This leads to increased use of evaporative cooling methods, where water is lost as vapor and cannot be reused. As a result, these centers consume significantly more water than those in cooler regions, such as Scandinavia. Humidity also plays an important role. In dry climates, evaporation is more efficient, which improves cooling performance but also increases water use.

The source and availability of water are also critical. Data centers in water-scarce regions often depend on municipal water supplies, which may already be under stress. This can lead to competition with local needs, such as access to drinking water or agricultural resources. A well-known example is Google’s data center in The Dalles, Oregon. The facility’s water use raised public concern, especially since the area was experiencing drought conditions at the time.

Additionally, the training of large AI models can lead to sudden spikes in water demand. These spikes may not last long, but they can still affect local water systems. Without proper planning and forecasting, this can result in a temporary imbalance in the water supply, including lower river levels or excessive groundwater extraction. Such changes can harm local ecosystems and reduce biodiversity.

To address these challenges, AI-related infrastructure planning must consider specific local factors such as temperature, water supply, and legal limits on usage. Sustainable deployment requires clear policies and a careful balance between technological growth and environmental protection. This includes collaborating with local communities, understanding regional water rights, and selecting suitable cooling systems that utilize water responsibly.

Corporate Commitments and Transparency Gaps

Major AI companies are becoming increasingly aware of their environmental impact and have pledged to improve their water management practices. Google, Microsoft, and Meta have each announced plans to become water-positive by 2030. This means they aim to restore more water than they consume across their global operations. Their efforts include watershed restoration, rainwater harvesting, greywater recycling, and support for local conservation projects.

Google plans to replenish 120% of the water it consumes. It publishes annual sustainability reports that include both usage and recovery figures. Microsoft has adopted adiabatic cooling systems, which reduce evaporation and can cut water use by up to 90% compared to traditional cooling towers. Meta has pledged to restore 200% of the water used in high-stress areas and 100% of the water used in medium-stress zones, focusing its efforts where water scarcity is most severe. Some data centers have also started using on-site reuse systems or rainwater collection to supplement their supply.

These commitments are relevant because the training and deployment of LLMs require powerful data centers. These operations consume large amounts of electricity and generate significant heat, thereby increasing the demand for water-intensive cooling. As AI services expand globally, particularly those involving LLMs, their environmental footprint grows as well. Responsible water use is becoming a critical part of sustainable AI development.

Cutting AI’s Water Footprint: Simple Steps and Collective Action

Reducing the water footprint of AI requires a combination of efficient technology, thoughtful planning, and shared responsibility. On the technical side, designing smaller and more efficient AI models is an important step. Methods like model pruning, quantization, and distillation help reduce model size and computational load. This reduces energy use and lowers the water required for cooling during both training and use.

Choosing the right time for training also matters. Running intensive workloads during cooler periods can reduce water lost through evaporation. The location of data centers also plays a role. Building facilities in areas with sustainable water resources or near renewable energy sources, such as wind and solar, can reduce the indirect water use associated with thermal power generation. Advances in AI algorithms, such as utilizing sparse attention or more efficient model designs alongside improved hardware, help reduce the overall environmental impact.

Tackling AI’s water footprint requires a collaborative effort that extends beyond technology companies. Governments play a key role in establishing rules that require transparent reporting of water use and promote consistent assessment standards. They can also make sustainable water sourcing a condition for approving new data centres. Environmental groups support this effort by monitoring claims, promoting stronger policies, and keeping the industry accountable. Local authorities should review infrastructure plans with water resources in mind, particularly in areas already experiencing stress.

Individual users also shape the direction of AI. By choosing platforms that report environmental data and commit to sustainability, they send a clear message about what matters. Developers and researchers must consider water consumption when evaluating AI systems. At the same time, universities and research centers can create tools to measure and reduce water use more accurately.

To make real progress, we must also focus on awareness and informed choices. Many people are unaware that even simple AI queries incur hidden environmental costs. When this becomes widely known, it encourages users to demand better practices and motivates companies to act responsibly. At the same time, the rapid expansion of large AI models continues to increase pressure on already limited freshwater supplies. This makes it essential to treat water use as a key part of AI’s overall environmental impact. Achieving meaningful change will require a collective effort from policymakers, developers, companies, and end users. If we make water stewardship a core part of how AI is designed and deployed, we can protect vital resources while still reaping the benefits of intelligent systems.

The Bottom Line

Reducing the water footprint of AI is no longer a secondary issue. It is a crucial component in developing sustainable technologies. Training and running large models takes a toll on freshwater supplies, especially in regions already facing climate stress.

To address this, we need smarter models, better hardware, and responsible data center planning. But real progress depends on more than just technology. Governments, companies, researchers, and users all play a role. Clear policies, transparent reporting, and public awareness can help drive better decisions. By incorporating water impact into our initial thinking about AI, we can prevent long-term harm to vital resources.