{"id":97890,"date":"2025-08-26T19:48:10","date_gmt":"2025-08-26T19:48:10","guid":{"rendered":"https:\/\/www.newsbeep.com\/au\/97890\/"},"modified":"2025-08-26T19:48:10","modified_gmt":"2025-08-26T19:48:10","slug":"whats-up-with-crusoes-atero-acquisition","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/au\/97890\/","title":{"rendered":"What’s Up With Crusoe’s Atero Acquisition?"},"content":{"rendered":"

Last month, we highlighted Crusoe’s construction ambitions<\/a>, developing enormous campuses to serve AI companies. Crusoe still describes itself as a cloud company, though\u2014a neocloud, offering GPU resources targeting AI and machine learning. As if to remind us all of those ambitions, Crusoe acquired stealth startup Atero last week, acquiring some cloud technology and expertise down at the chip level. <\/p>\n

To be fair, Crusoe has voiced its cloud ambitions loudly for some time. But the Crusoe Cloud business\u2014the public cloud offering GPU rentals for AI and machine learning\u2014is relatively small and new. General availability was announced in December alongside Crusoe’s $600 million Series D<\/a>.<\/p>\n

Datacenter campuses are relatively new for Crusoe too, but they generate the big numbers that grab more attention. The headliners are the 1.2 GW campus in Abilene, Texas and the accompanying $15 billion joint venture with alternative asset manager Blue Owl Capital. That project is being built out for OpenAI. (The datacenters are leased to Oracle, which provides the cloud services to OpenAI.) More recently, Crusoe announced a Wyoming campus that will start at 1.8 GW of capacity<\/a> and is designed for as much as 10 GW.<\/p>\n

Deep Into the Cloud<\/p>\n

If those buildouts are the “macro” side of neocloud operations, the Atero acquisition represents the micro side, getting into what’s happening at the chip level. Atero focuses on GPU management, including GPU memory management. Crusoe’s press release<\/a> says the technology will improve GPU utilization, partly by placing data more optimally. Part of the issue with utilization is that GPUs need to wait for data, often due to bandwidth constraints.<\/p>\n

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