Nvidia is increasing its bet on CoreWeave with a new $2 billion investment, strengthening its partnership with the fast-growing artificial-intelligence data-center operator that relies heavily on Nvidia chips. The new funding adds to Nvidia’s earlier backing and shows its aim to secure more infrastructure for the fast-expanding market for AI computing.
Under the latest agreement, Nvidia and CoreWeave plan to build a network of AI factories that will run on Nvidia’s computing-platform technology and be operated by CoreWeave. These facilities are designed to provide large-scale processing power for training and running AI models for businesses, cloud providers, and other customers that need powerful computing but do not want to build and manage such systems themselves.
The companies said the new plants are expected to reach about 5 gigawatts of capacity by 2030, highlighting how quickly demand for AI services is growing. That level of capacity will require major investments in land, electricity, and advanced hardware, including Nvidia’s high-end processors that power many of today’s leading AI systems.
Nvidia said it plans to use its financial strength and market position to help secure key resources for the projects, such as data-center sites and long-term power deals with utilities and energy suppliers. This support is intended to make it easier and faster for CoreWeave to expand in a market where power availability and suitable locations for large data centers are often limited.
The deal deepens Nvidia’s role not only as a chip supplier but as a central player in the broader AI infrastructure ecosystem, while giving CoreWeave additional capital and credibility as it competes with bigger cloud and data-center rivals. For customers, the planned AI factories are meant to offer flexible access to advanced computing, letting them rent capacity instead of spending heavily to build their own facilities. The move highlights how partnerships between chip makers and specialized data-center operators are becoming a key way to scale AI infrastructure as costs and complexity keep rising.
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