Nvidia added “fastest-growing” to its claim of being the world’s largest networking company. Its interconnectivity business had yet another record quarter, with revenue growing 18% on a sequential basis.

The tech giant’s earnings continue to boggle the mind: gross margins of 75% for the second quarter ending July 26, 2026. Revenues totaled $96.2 billion, up 18% on the previous quarter and 106% year over year (YoY).

Nvidia’s networking products are helping to push revenue even higher, with its Spectrum-X Ethernet products growing 2.6-times on a YoY basis.

CFO Colette Kress told investors that Spectrum-X demand was “helping us become the largest and fastest-growing network company in the world,” while agentic AI adoption was tipped to push demand even further.

While the world routinely waits for Nvidia earnings with bated breath, its latest earnings showed no signs of wobbling. Kress said that since its Hopper generation, the company’s revenue opportunity has grown from roughly $18 billion per gigawatt to $25 billion with Blackwell – with that number jumping to $40 billion for the forthcoming Vera Rubin.

“Our ability to extreme co-design across graphics processing units, central processing units, NVLink scale-up networking, scale-out networking, systems, algorithms, and software enables us to deliver X-factor performance gain every generation,” the CFO said.

In terms of market penetration, Kress revealed that neoclouds, enterprise edge, and “sovereign”-related deployments represent roughly half of its data center business, in addition to high demand from hyperscalers.

Looking ahead to the third quarter, Nvidia expects total revenues to be at $108 billion, “plus or minus 2%,” with continued growth from hyperscalers and non-high-end players.

“We see Vera Rubin accounting for about 20% of data center revenue in Q3,” Kress told investors.

Nvidia execs on the earnings call were bullish when addressing supply chain concerns amid the compounding memory crunch, which has resulted in the company raising prices. Kress said the AI buildout driving memory scarcity “is a symptom of the same demand surge that's driving our own growth.”

Founder and CEO Jensen Huang – renowned for his emphasis on supply chain management – said the company boasts “a lot greater visibility now, upstream and downstream” and that the “reinvention” of Nvidia’s supply chain for its flagship NVLink 72 rack-scale systems helped drive revenue opportunities later down the line.

“Just as a long time ago, people asked me why is it that we're working with memory suppliers when we're a chip company. And today, people understand it's really quite genius that we were working on our supply chain so far upstream,” Huang said. “We work with power generator companies. ... We work with land, power, and shell companies all around the world. And that helps prepare all of this computing that's going to be built that will ultimately deploy for our ecosystem and our customers.”

Can’t slow down? Nvidia won’t slow down

Shay Boloor, chief market strategist at Futurum Equities, said Nvidia delivered exactly the kind of quarter that “makes it difficult to argue AI infrastructure demand has already peaked.”

“[The] most important part of the quarter for me is that the demand base is actually broadening because hyperscale revenue grew 102% YoY and 13% sequentially to $49 billion while ACIE [AI clouds, industrial, and enterprise] grew 138% YoY and 25% sequentially to $40 billion. This shows me AI clouds, enterprises, industrial customers, and sovereign buyers are now nearly as large as hyperscalers and growing materially faster,” the analyst noted.

Emarketer analyst Jacob Bourne added that the ramp of Vera Rubin’s release alongside a strong outlook suggests demand will continue even without meaningful revenue from export restricted customers in China.

“Nvidia still faces a changing competitive landscape, with some of its largest customers developing their own chips while startups target parts of the AI compute market,” the analyst explained in emailed quotes to SDxCentral. “Yet Nvidia continues to deepen its relevance across the AI stack, expanding beyond training into inference, models, networking, and broader AI infrastructure. Its ability to adapt as AI workloads and competitive dynamics evolve provides a strong argument for the durability of its position.”