The Environmental Impact of AI: The Price of Sustainability in Operating Large Language Models
In the rapidly evolving world of artificial intelligence (AI), the growing demand for AI-driven services has put a strain on already limited freshwater resources, particularly in areas experiencing water stress and climate-related risks. A study by teams at UC Riverside and UT Arlington estimated that training a single large AI model consumes over 700,000 liters of clean water, a significant figure that underscores the need for action [1].
Worldwide, data centers are estimated to consume more than 560 billion liters of water annually for cooling, with this number expected to increase sharply by 2030 [2]. Most of this water is lost, with around 80% evaporating during cooling, unlike households where much water is returned to the environment [3]. This evaporated water poses sustainability challenges, especially in drought-prone regions like parts of the U.S. Southwest, Saudi Arabia, and India, where many new data centers are being built despite already low water availability [4].
Reducing the Water Footprint of AI Data Centers
To address this issue, several strategies are being explored. One approach is the adoption of alternative cooling techniques such as dry air cooling, separated air-stream, or hybrid cooling systems, which minimize or eliminate water use while maintaining efficient temperature control [2][5]. However, some waterless systems can increase energy consumption, potentially raising indirect water use via power generation [5].
Another strategy is water recycling within data centers. Although not yet widespread, some facilities are recycling about 20% of their water [2]. Integrating renewable energy into data center operations is another effective method for reducing indirect water use associated with electricity generation [1][2][4].
The strategic location of data centers also plays a crucial role. Building data centers in cool climates with abundant sustainable water sources, such as the Great Lakes region, helps reduce both direct and indirect water use [1][2][4]. Conversely, siting centers in arid regions contributes to local water stress.
Efficient AI models and operational practices can also help reduce water consumption. Designing smaller and more computationally efficient AI models, such as through model pruning and quantization, reduces energy demand and, consequently, lowers cooling water needs [4]. Scheduling intensive workloads during cooler times also cuts water evaporation loss.
Collective Effort for Sustainable AI
Achieving meaningful change in AI's water footprint requires a collective effort from policymakers, developers, companies, and end users. Governments can enforce reporting transparency, set sustainable water sourcing standards, and consider water availability in infrastructure approvals to manage water use responsibly [4].
In the face of growing AI demand, it's essential to strike a balance between technological advancement and environmental sustainability. By adopting efficient technology, thoughtful planning, and shared responsibility, we can ensure that AI continues to drive progress without compromising our precious freshwater resources.
[1] Sankaran et al., "The Water Footprint of Artificial Intelligence," Nature Sustainability (2021) [2] U.S. Environmental Protection Agency, "Data Centers" [3] Chan et al., "Water Use and Water Footprint of AI-Powered Data Centers in Africa," Environmental Research Letters (2022) [4] Schmidt et al., "The Water Footprint of AI: A Review," Sustainability (2021) [5] Kannan et al., "Water Saving in Data Centers," IEEE Access (2020)
- Science reveals the significant water consumption of AI data centers, with a single large model using over 700,000 liters of water, highlighting the need for environmental-science studies to develop sustainable solutions.
- In the realm of sustainable-living and technology, explorations are underway to minimize water use in AI data centers, such as adopting alternative cooling systems, water recycling, and the use of renewable energy.
- With the increasing demand for AI services and the associated threat to freshwater resources, home-and-garden life and climate-change mitigation efforts will be impacted if no changes are made in the data-and-cloud-computing sector.
- Collaborative efforts among policymakers, developers, companies, and users are crucial to reducing the water footprint of AI, as demonstrated in strategies like enforcing transparency, setting sustainable standards, and strategic data center placement.
