‘There are 250,000 enterprise companies around the world. Many of them will have to build or want to build AI factories for themselves to operate. Many industrial companies, there’s no choice but to put the computer where the context is, where the action is. You can’t put that in the cloud. It has to respond reliably, quickly every single time,’ says Nvidia President and CEO Jensen Huang.
Nvidia’s dual data center strategy is helping the company not only with current growth but is laying the foundation for continued growth into the foreseeable future, according to Nvidia President and CEO Jensen Huang.
Huang Wednesday told analysts and investors during company’s first fiscal quarter 2027 quarterly financial conference call Wednesday that his company has segmented its data center business into two main parts for simplicity’s sake.
The first segment serves the hyperscaler business, which is seeing huge growth and expecting CapEx spending of a trillion dollars this year, Huang said.
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“I have every expectation it’s going to grow from here for fundamentally good reasons,” he said. “This is the way computing is going to work in the future, and if they don’t have the compute, they won’t have the revenues. It is very clear: compute is revenues. Compute is profit. And so the world is changing. SaaS didn’t used to use as much compute, but AI requires a tremendous amount of compute. But you could do, of course, incredibly more, which is the reason why we heard about the frontier AI companies, both Anthropic and OpenAI, growing at an incredible pace. The fact that they can grow within one month what some of the SaaS companies would have taken a decade to grow tells you something.”
The second major segment is the AI-native clouds that Huang said are regional.
“There are startups all over the world supporting those companies,” he said. “There are 250,000 enterprise companies around the world. Many of them will have to build or want to build AI factories for themselves to operate. Many industrial companies, there’s no choice but to put the computer where the context is, where the action is. You can’t put that in the cloud. It has to respond reliably, quickly every single time. I can’t imagine a chip plant being connected to a cloud service provider. It doesn’t make any sense.”
Within that is a whole of data centers where semi-custom chips don’t make sense because those data centers want to operate systems and not design and build their own systems, Huang said.
“The second category is extremely diverse,” he said. “Instead of five or six or seven companies representing the revenues associated with our first category, the second category is hundreds, thousands of companies, and in the future would be hundreds of thousands of companies. A large number of companies with smaller installations. And that category is going to continue to grow at incredible pace.”
Nvidia is fairly unique in its ability to serve this industry because of its platform which Huang said is built like it’s vertically integrated, so that everything works together, or it can be disassembled so customers can build and buy it in the configuration they want.
“This second category is fairly poorly understood, because there are just so many companies, and each one of the installations are relatively small compared to, of course, one of the hyperscalers,” he said. “And so if you look at the segmentation and the size of each, you could see that in fact we’re growing share in the hyperscalers because we now have much bigger support from Anthropic [while] very few companies have exposure into the second category.”
Nvidia has also become a platform on which to build neoclouds and other AI-native clouds, Huang said.
“AI-native clouds don’t build chips, don’t design their own chips, and they don’t want to,” he said. “They can’t really assemble unrelated parts together into an AI factory, and their tolerance for time to first token is extremely low. And their need for an architecture that has a great deal of off-take so that it runs every model, has customers from everywhere, is incredibly high, and so that’s the reason why Nvidia’s architecture is so perfect for them. We offer every component, and whatever we don’t offer, our ecosystem of partners offers it, and it’s all fully integrated. All works together.”
The number of customers that can rent capacity from AI-native cloud providers is incredibly high, Huang said.
“Basically, every single AI builder, every AI-native startup around the world, SaaS companies, enterprise companies, industrial companies,” he said. “And so our computing, our architecture, is the most rentable of any computing platform in the world. It’s the most performant, it’s the easiest to put together, it’s the most rentable, has the best TCO, and it’s the easiest to finance. All of those properties are quite unique to the needs of AI natives.”
Nvidia The Dominant Enterprise Infrastructure
Nvidia has become the dominant new architecture inside of corporate enterprise infrastructure, but it’s being delivered by the kinds of companies channel partners have all worked with for a long time, said Shawn O’Grady, chair and CEO of General Datatech, a Dallas-based solution provider and Nvidia channel partner that focuses on mission-critical infrastructures and AI. General Datatech is ranked No. 51 on CRN’s 2025 Solution Provider 500.
“It’s being delivered by Dell and HPE and Lenovo and Cisco,” O’Grady told CRN. “In storage, there are obviously new players we work with there with Nvidia like Vast Data, but our traditional storage partners like NetApp and Everpure also have architected their storage infrastructures in order to handle the massive data that’s being driven by AI. We spent a lot of time with Nvidia directly, even though we sell through their OEM partners.”
Nvidia also works a lot on the networking side with OEM partners like Cisco, particularly in Cisco’s Mass Scale Infrastructure group, which is used in the optical technology that General Datatech brings to its carrier customers, O’Grady said.
“We’re starting to see a need for networking of that substance, specifically in AI workloads,” he said. “In a lot of ways, we’re building on strengths that we’ve had, not having to create new stuff to monetize this opportunity. But it has required us to keep very current on new technologies. In some cases, this means developing new partner relationships like with Nvidia, but in a lot of ways, we’re building on a core strength.”
Nvidia By The Numbers
For its first fiscal quarter 2027, which ended April 26, Nvidia reported a record revenue of $81.62 billion, up 85 percent over the $44.06 billion the company reported for its first fiscal quarter 2026.
That included record data center revenue of $75.2 billion, up 92 percent over last year, as well as edge computing revenue of $6.4 billion, up 29 percent. The company’s data center revenue includes revenue from hyperscale, AI clouds, industrial, and enterprise.
Total revenue for the quarter beat analyst expectations by $2.65 billion, according to Seeking Alpha.
The company also reported GAAP net income of $58.3 million or $2.39 per share, more than double last year’s $18.8 million or 76 cents per share.
On a non-GAAP basis, Nvidia reported net income of $45.5 million or $1.87 per share, more than double last year’s $19.1 million or 78 cents per share.
Non-GAAP earnings beat analyst expectations by 10 cents per share, according to Seeking Alpha.
Looking ahead, Nvidia expects to report revenue of $91.0 billion plus or minus 2 percent during its second fiscal quarter 2026 compared to last year’s $46.7 billion. That number does not include possible revenue from China.
