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Nvidia has secured $500 billion in capital from a consortium of major Wall Street investors, including Apollo,BlackRock, Blackstone, Brookfield, Goldman Sachs,and KKR, to finance the expansion of artificial intelligence (AI) infrastructure. The chipmaker announced the deals,marking a significant shift as these long-term capital providers begin to treat AI hardware and infrastructure, often termed “compute,” as a distinct asset class. This new funding mechanism aims to accelerate the development of physical backbone required for the global AI boom.
The substantial financing will support both Nvidia's internal projects and those developed by its partners. A primary focus will be construction of new data centers, designed to house, operate,and cool the extensive networks of stacked computer chips essential for processing AI data and actions. These facilities are crucial for the continuous operation of AI models. Funds will also back new factories dedicated to manufacturing the specialized AI chips required to power these advanced systems,aiming to increase their availability to a growing market and meet escalating demand.
Jensen Huang, Nvidia's chief executive,emphasized the direct link between processing power and financial returns .
In AI, compute is revenue. We are bringing the world's leading long-term capital providers together to independently underwrite AI infrastructure.
Huang added in a statement on Monday that while Nvidia began as a chip-maker, it is now “helping create a new class of productive, investable infrastructure: AI factories.” This vision positions the company beyond just chip manufacturing,into building entire ecosystem.
The move reflects broader industry recognition of AI's foundational components as critical long-term investments. Joe Bae and Scott Nuttall,co-chief executives of KKR,underscored the practical challenges involved in scaling such operations .
Compute has become a critical infrastructure asset. As we've scaled our approach to digital infrastructure,we've learned that delivery, not ambition, is the hard part.
Their comments highlight the complexities of deploying and managing vast computing resources.
Nvidia's graphics processing units (GPUs) are central to nearly every major technology and AI company, powering services,AI platforms,and chatbots. These specialized processors are uniquely efficient at handling the parallel computations required for machine learning. Prominent users of these chips include Google
