Chinese companies have committed to building data center projects with a combined estimated capacity of 12.5 gigawatts in Ulanqab, a city of roughly 1.5 million people in Inner Mongolia, according to a research note published by Goldman Sachs last week. Over 70 percent of these total commitments were announced in just the past year, making the location one of the fastest growing compute clusters in Asia. The surge transforms what has long been China's heartland for sheep farming and coal mining into the country's hottest destination for AI infrastructure.
Since 2016, nearly 100 data centers have been opened or begun construction in Ulanqab, with major Chinese AI companies now making significant investments in their own infrastructure rather than renting compute from cloud providers. DeepSeek is reportedly constructing a massive AI data center in the city, alongside ByteDance, Alibaba, and Xiaohongshu. For years, Chinese AI firms spent far less on building physical infrastructure than their American counterparts, despite developing numerous popular AI models with impressive capabilities. The city sits at high elevation on the Inner Mongolian Plateau with long, cold winters, meaning data centers require less energy to stay cool. Two dedicated fiber optics cables built in 2017 and 2019 reduced average latency speeds to under five milliseconds, fast enough to support real-time data exchanges like AI inference.
According to Andrew Stokols, a professor at Singapore Management University who studies China's compute infrastructure, "with the rise of AI in 2022, there was the realization that actually, those remote data centers could be well-utilized for model training." The report notes that Ulanqab's growth appears to have been driven more by commercial demand than government-led investment, as Chinese AI startups like DeepSeek, Moonshot AI, and Zhiput AI attract more paying users at home. Damien Ma, director of Carnegie China, a Singapore-based research center, says there's a positive correlation between regions in China with the most unused renewable energy and those that have built the most data centers.
The appeal largely comes down to cost. Electricity is cheaper in Inner Mongolia than almost anywhere else in China, driven by both the strong growth of wind and solar energy and an abundant supply of coal. The Chinese government views data center construction in Inner Mongolia as a strategy to absorb the country's excess capacity of renewable energy while catching up to the pace of data center construction in the US. Envision, one of China's largest manufacturers of wind turbines, announced this month it will build a 2 gigawatt AI data center in Ulanqab connected directly to the company's own clean power supply. However, Stokols found in his research that about 37 percent of electricity in Ulanqab still comes from coal, and since data centers need to run around the clock, operators have traditionally favored fossil fuels for their reliability.
There's a significant challenge on the horizon: water scarcity. Ulanqab is about as dry as Denver, receiving only roughly 14 inches of rain each year, and the local government already struggles to provide enough water to meet resident demand before many planned data center projects are even operational. Last month, the local water company was forced to turn off several waterworks for seven hours each night to mitigate peak demand. Weather data from the local government shows data centers only require additional water for cooling during two months out of the year, but all the new infrastructure could still pose a considerable environmental challenge for the region. The region is now racing to replace coal with wind and solar, but it remains unclear how fast that transition will happen or how far it will go, with Ma suggesting that "in three years, maybe it will be completely powered by renewables." The buildout reveals how infrastructure constraints can emerge faster than the renewable transition needed to support them, particularly when commercial momentum concentrates in a single location for cost advantages that may prove temporary.