Anthropic and OpenAI are turning to smaller artificial intelligence data center deals as they race to secure the computing capacity needed to serve rapidly growing demand, signaling a shift in the infrastructure market from a handful of massive campuses toward more distributed deployments.
The two AI labs have signed some of the industry’s largest infrastructure agreements over the past year, covering facilities with hundreds of megawatts and, in OpenAI’s case, gigawatts of planned capacity. But sources familiar with the companies’ discussions told CNBC that both are now exploring much smaller deployments of roughly 20 to 30 megawatts.
Anthropic has approached potential partners about capacity in that range across the U.K. and the Nordic countries, according to four people familiar with the discussions. OpenAI has also explored similar opportunities in the Nordics, two of the sources said.
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One source said they were also familiar with discussions involving both companies over U.S. deployments of comparable size.
The smaller deals could allow the AI companies to get computing resources into operation sooner, rather than waiting for enormous data center projects that can take years to develop and increasingly face constraints involving electricity, land, permitting and community opposition.
“We’re building a diversified compute portfolio to meet growing demand for AI around the world,” an OpenAI spokesperson told CNBC.
“Different workloads need different infrastructure, so we have conversations with a range of partners and assess opportunities based on our requirements, performance, reliability, timing and cost,” the spokesperson added, while declining to comment on specific commercial discussions.
AI Infrastructure Strategy Is Changing
The search for smaller capacity comes after a period in which AI companies aggressively pursued enormous, long-term infrastructure commitments.
Anthropic signed a roughly $45 billion cloud agreement with Nscale that is expected to provide about 460 megawatts of compute capacity at a data center development in West Virginia, according to people familiar with the deal.
OpenAI has also dramatically expanded its infrastructure ambitions. The company said in April that it had exceeded the original 10-gigawatt commitment associated with its Stargate AI infrastructure project. It has subsequently committed to developing another 3 gigawatts in Georgia and 8 gigawatts in Ohio.
Those projects illustrate the enormous amount of computing power required to train and deploy increasingly capable AI models. But they also expose the industry’s growing dependence on projects that require large amounts of electricity, land, and capital.
Large data center developments in the United States have encountered increasing opposition from local communities, while European markets face their own constraints. Limited availability of suitable land and power is making it more difficult for operators to deliver huge blocks of capacity quickly.
That is creating an opening for smaller facilities.
“Securing a few megawatts at an existing powered site can be more practical than waiting for a much larger block in one location,” said Jabez Tan, head of research at Structure Research.
“For workloads that can operate across separate sites, a collection of smaller deployments can add up to substantial capacity,” he added.
The economics are deemed relevant because not every AI workload requires a giant cluster operating in a single location. Training a frontier model requires large numbers of chips to work together and exchange data at high speeds. Serving that model to users is different. Inference, the process of generating responses from a trained model, can often be distributed across multiple smaller clusters.
“Training a large model typically requires many chips working closely together,” Tan said. “Many inference workloads can instead serve separate requests across multiple smaller clusters, opening up more locations.”
That is becoming increasingly important as AI companies move from building models to serving them at enormous scale.
Inference Is Becoming the New Infrastructure Race
The economics of AI infrastructure are increasingly being shaped by inference. The computing requirements of training remain enormous, but once models are deployed, every query, coding request, image generation, or other AI interaction consumes computing resources. As adoption increases, the infrastructure required to serve those requests can become a substantial and recurring demand on data centers.
Real estate company JLL expects inference workloads to overtake training workloads as a share of total data center capacity in 2027.
In 2025, inference accounted for 9% of global data center workloads, compared with 14% for training, according to JLL. By 2030, inference is projected to account for 37% of capacity, while training is expected to represent 13%.
That shift changes what AI companies need from infrastructure providers.
For model training, concentrating thousands of chips in a massive facility can be essential. But geographic distribution can provide greater flexibility for inference, allowing workloads to be spread across several locations and potentially bringing capacity online faster. It also means AI companies may compete more for existing powered sites rather than simply waiting for new megaprojects to be completed.
Nvidia has already been examining this trend. In February, the chipmaker said it would work with several data center stakeholders to study smaller facilities designed for distributed inference.
Crusoe, a major AI infrastructure provider that built a large data center complex in Texas used by OpenAI, is also moving toward smaller facilities, according to a Wall Street Journal report. Those facilities are expected to be faster and cheaper to build than larger projects that are facing delays across the United States.
The shift comes as demand for so-called neocloud infrastructure continues to surge. These specialized cloud providers have benefited from AI companies seeking dedicated access to GPUs and other computing infrastructure without having to build and operate every facility themselves.
Crusoe highlighted the strength of that demand Thursday when it announced a $3.9 billion funding round at a post-money valuation of $30.9 billion.
The End of the Mega-Data Center?
The growing interest in 20- to 30-megawatt deployments does not mean the industry’s enormous data center projects are disappearing.
OpenAI and Anthropic still require massive amounts of computing power, particularly for training increasingly sophisticated models. Their multibillion-dollar infrastructure commitments remain evidence of how much capacity the AI industry expects to consume.
What is changing is the composition of that demand.
Instead of treating AI infrastructure as a race to secure the largest possible single site, companies can assemble capacity from multiple facilities, provided their workloads can be distributed efficiently. That could become valuable in markets where securing hundreds of megawatts of new power in one location is difficult.
Smaller projects can also offer data center operators a faster route to revenue. Existing sites with power already available can potentially be upgraded or equipped for AI workloads without waiting for an entirely new campus to be constructed.
The result could be a more fragmented AI infrastructure landscape, with enormous training campuses operating alongside networks of smaller inference facilities. For Anthropic and OpenAI, the strategy is ultimately about one thing: usable computing capacity.
The industry’s biggest infrastructure projects may attract the most attention because of their size and multibillion-dollar price tags. But as AI moves from the training phase into everyday commercial use, the ability to secure smaller amounts of power and compute quickly could become just as important.