Jensen Huang Says Nvidia's Next-Gen Vera Rubin Chips Will Generate $40 Billion in Revenue per Gigawatt, Up From $25 Billion for Grace Blackwell. What That Means for NVDA's Growth Runway.
I'm LongbridgeAI, I can summarize articles.Nvidia CEO Jensen Huang announced that the next-generation Vera Rubin architecture will generate $40 billion in revenue per gigawatt, a 60% increase over the Grace Blackwell platform. This efficiency gain addresses power constraints in AI data centers. Vera Rubin has entered production with orders from major hyperscalers. Huang predicts AI infrastructure spending could reach $3-$4 trillion by 2030, and Nvidia expects to double chip sales volume in 2027 compared to 2026, extending its growth runway beyond current massive revenues.
Nvidia (NVDA -0.59%) has already made an incredible amount of money selling the powerful chips that drive artificial intelligence (AI). But CEO Jensen Huang believes the company's biggest opportunities are still ahead, and Nvidia's newest technology could help him prove it.
The chipmaker's next-generation Vera Rubin architecture is designed to deliver substantially more computing power from the same amount of electricity. That's important for cloud companies that are racing to build AI data centers, where access to power has become a major constraint.
But there's another benefit for Nvidia shareholders. Vera Rubin could generate 60% more revenue per gigawatt of computing capacity than its predecessor.
And that helps explain why Huang is so confident about Nvidia's growth runway.
Nvidia CEO Jensen Huang. Image source: Getty Images.
Nvidia is getting more valuable with every generation
Nvidia's Hopper and Blackwell graphics processing units (GPUs) have been enormously successful with hyperscalers and smaller cloud providers building artificial intelligence computing capacity.
But Nvidia isn't simply selling faster GPUs anymore. Its Vera Rubin platform combines Rubin GPUs, Vera central processing units (CPUs), high-speed NVLink connections, networking equipment, and software into a complete AI computing system.
NVLink enables processors to communicate and share memory access more efficiently, helping customers complete more computing work without a corresponding increase in electricity consumption.
During Nvidia's fiscal second-quarter 2027 earnings call in August, Huang explained how that expanding platform translates into a bigger revenue opportunity. "In the case of Hopper, we were at about $18 billion per gigawatt. For Grace Blackwell, we're at about $25 billion per gigawatt. And for Vera Rubin, it's about $40 billion per gigawatt," he said. "And the productivity is X-factors increase in each generation."
That's a 60% increase over Blackwell, even if customers don't build any additional power capacity.
And the technology is already moving into the market. Nvidia says Vera Rubin has entered production shipments, with purchase orders from major hyperscalers, AI cloud providers, and equipment manufacturers.
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Why Jensen Huang sees more growth ahead
Huang predicted last year that AI infrastructure spending could reach $3 trillion to $4 trillion by 2030, and he reiterated that forecast in September. He's also expecting Nvidia to sell twice as many chips in 2027 as in 2026, according to comments he made to reporters in Scotland on Sept. 17.
That doesn't necessarily mean Nvidia's revenue will double. Chip volumes and revenue are different measurements, and the $40 billion-per-gigawatt figure represents a potential sales opportunity rather than guaranteed orders.
But the underlying business is already growing at an extraordinary pace. Nvidia reported $96.2 billion in revenue for its fiscal second quarter, which ended July 26, up 106% from a year earlier. Data center revenue alone reached $89 billion.
Vera Rubin gives Nvidia another way to increase revenue beyond simply selling more GPUs. If customers continue to expand their AI capacity while spending more per gigawatt on Nvidia technology, the company's growth runway could be considerably longer than its already massive size suggests.