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# Anthropic and OpenAI hunt for smaller data center deals, sources tell CNBC, in race to deploy AI capacity
Published Fri, Sep 18 20265:38 AM EDT
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Key Points
- Anthropic and OpenAI are exploring opportunities for smaller data center deals, sources told CNBC.
- Both companies are racing to deploy AI capacity and have announced a flurry of AI infrastructure deals over the past year as demand booms.
- Smaller capacity deals are often attractive because of "speed to usable capacity," one analyst told CNBC.
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Anthropic and OpenAI are hunting for smaller AI data center deals, sources told CNBC, as the race to access the infrastructure needed to deploy workloads ramps up.
The two AI labs have both inked [huge deals](https://www.cnbc.com/2026/07/27/nvidia-and-openai-in-talks-for-up-to-250-billion-dollar-ai-backstop.html) for AI data centers in the past year for facilities of multi-hundred-megawatt and gigawattcapacity, but sources have said those companies are now also looking for compute capacity deals for much smaller deployments of 20-30 MW.
Anthropic has sounded out agreements within that range across the U.K. and the Nordics, four people familiar with the conversations, who asked to remain anonymous when discussing private business dealings, told CNBC. OpenAI had been exploring opportunities for those smaller capacity deployments in the Nordics, two of the sources said.
One source said they were also familiar with talks involving Anthropic and OpenAI about U.S. capacity deployments at that scale.
Both companies have announced a flurry of AI infrastructure deals over the past year as they've looked to train and serve their models to end users. Deals to secure smaller allocations of compute allow companies to deploy workloads faster amid the AI boom.
"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," they added. "We don't comment on specific commercial discussions."
Anthropic did not comment when approached by CNBC.
## 'Speed to usable capacity'
Both AI labs typically rent compute capacity from data center operators and neoclouds and have sought large-scale, long-term agreements.
Anthropic inked a roughly [$45 billion cloud deal](https://www.cnbc.com/2026/08/26/anthropic-and-nscale-strike-45-billion-cloud-deal-sources-say.html) with Nscale, which will see the AI lab rent around 460 MW of compute capacity at a data center development in West Virginia, two people familiar with the matter told CNBC in August.
OpenAI has said it surpassed the original commitment of 10 GW to its Stargate AI infrastructure project in April and has since committed to developing a further 3 GW in Georgia and [8 GW in Ohio.](https://www.cnbc.com/2026/08/17/nvidia-financing-open-ai-data-center-ohio.html)
Huge data center projects in the U.S. and further afield are [increasingly facing pushback](https://www.cnbc.com/2026/09/06/ai-data-centers-are-transforming-rural-land-markets-fueling-backlash.html) from local communities. The sector is also under pressure in much of Europe, where available land and power are in short supply.
CoreWeave CEO: AI industry has not done a good job explaining data center impact on communities
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CoreWeave CEO: AI industry has not done a good job explaining data center impact on communities
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Smaller capacity deals are often attractive because of "speed to usable capacity," Jabez Tan, head of research at Structure Research, told CNBC.
"Securing a few megawatts at an existing powered site can be more practical than waiting for a much larger block in one location," he said. "For workloads that can operate across separate sites, a collection of smaller deployments can add up to substantial capacity."
## Shift to inference
Training AI models requires large amounts of computing power to process huge quantities of data, but deploying those systems day-to-day — a processknown as inference — can be done with smaller clusters of chips.
"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."
The shift matters as more AI compute moves from training models to serving them in production. The amount of capacity being used to serve inference is therefore expected to rise.
The proportion of total data center capacity used for inference workloads is expected to overtake training workloads in 2027, according to a report by real estate company JLL. In 2025, inference made up 9% of global workloads in data centers compared to 14% for training, the report said. By 2030, inference is projected to use 37% of that capacity, compared to just 13% for training.
In February, it was announced that [Nvidia](https://www.cnbc.com/quotes/NVDA/) would collabora