Evaporative Cooling Towers in the Data Center Process

Cooling towers get the job done, but they require immense amounts of water to do so. The researchers estimate around a gallon of water is consumed for every kilowatt-hour expended in an average data center. Not just any type of water can be used, either. Data centers pull from clean, freshwater sources in order to avoid the corrosion or bacteria growth that can come with seawater. Freshwater is also essential for humidity control in the rooms. The researchers likewise hold data centers accountable for the water needed to generate the high amounts of electricity they consume, something the scientists called “off-site indirect water consumption.”


Google data center images

Water consumption issues aren’t limited to OpenAI or AI models. In 2019, Google requested more than 2.3 billion gallons of water for data centers in just three states. The company currently has 14 data centers spread out across North America which it uses to power Google Search, its suite of workplace products, and more recently, its LaMDa and Bard large language models. LaMDA alone, according to the recent research paper, could require millions of liters of water to train, larger than GPT-3 because several of Google’s thirsty data centers are housed in hot states like Texas; researchers issued a caveat with this estimation, though, calling it an “ approximate reference point.”


Aside from water, new LLMs similarly require a staggering amount of electricity. A Stanford AI report released last week looks at differences in energy consumption among four prominent AI models, estimating OpenAI’s GPT-3 released 502 metric tons of carbon during its training. Overall, the energy needed to train GPT-3 could power an average American’s home for hundreds of years.

“The race for data centers to keep up with it all is pretty frantic,” Critical Facilities Efficiency Solution CEO Kevin Kent said in an interview with Time. “They can’t always make the most environmentally best choices.”


Climate change and worsening droughts could amplify concerns over AI’s water usage

Already, the World Economic Forum estimates some 2.2 million US residents lack water and basic indoor plumbing. Another 44 million live with “inadequate” water systems. Researchers fear a combination of climate change and increased US populations will make those figures even worse by the end of the century. By 2071, Stanford estimates nearly half of the country’s 204 freshwater basins will be unable to meet monthly water demands. Many regions could reportedly see their water supplies cut by a third in the next 50 years.


Rising temperatures partially fueled by human activity have resulted in the American West recording its worst drought in 1,000 years which also threatens freshwater, though recent flooding rains have helped stave off some dire concerns. Water levels at reservoirs like Lake Mead have receded so far that they’ve exposed decades old human remains. All of that means AI’s hefty water demands will likely become a growing point of contention, especially if the tech is embedded into ever more sectors and services. Data requirements for LLMs are only getting larger, which means companies will have to find ways to increase their data centers’ water efficiency.

Researchers say there are some relatively clear ways to bring AI’s water price tag down. For starters, where and when AI models are trained matters. Outside temperatures, for example, can affect the amount of water required to cool data centers. AI companies could hypothetically train models at midnight when it’s cooler or in a data center with better water efficiency to cut down on usage. Chatbot users, on the other hand, could opt to engage with the modules during “water-efficient hours,” much as municipal authorities encourage off-hours dishwasher use. Still, any of those demand-side changes will require greater transparency on the part of tech companies building these models, something the researchers say is in worryingly short supply.


“We recommend AI model developers and data center operators be more transparent,” the researchers wrote. “When and where are the AI models trained? What about the AI models trained and/or deployed in third-party colocation data centers or public clouds? Such information will be of great value to the research community and the general public.”

Want to know more about AI, chatbots, and the future of machine learning? Check out our full coverage of artificial intelligence, or browse our guides to The Best Free AI Art Generators and Everything We Know About OpenAI’s ChatGPT.