But there’s another benefit for Nvidia shareholders. Vera Rubin could generate 60% more revenue per gigawatt of computing capacity than its predecessor. <\/p>\n
And that helps explain why Huang is so confident about Nvidia’s growth runway<\/a>. <\/p>\n Nvidia’s Hopper and Blackwell graphics processing units (GPUs) have been enormously successful with hyperscalers and smaller cloud providers building artificial intelligence computing capacity. <\/p>\n 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. <\/p>\n NVLink enables processors to communicate and share memory access more efficiently, helping customers complete more computing work without a corresponding increase in electricity consumption. <\/p>\n 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.” <\/p>\n That’s a 60% increase over Blackwell<\/a>, even if customers don’t build any additional power capacity. <\/p>\n 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. <\/p>\n Why Jensen Huang sees more growth ahead <\/p>\n 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. <\/p>\n 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. <\/p>\n 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. <\/p>\n 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. <\/p>\n Should you buy stock in Nvidia right now? <\/p>\n Before you buy stock in Nvidia, consider this: <\/p>\n The Motley Fool Stock Advisor analyst team just identified what they believe are the 10 best stocks<\/a> for investors to buy now\u2026 and Nvidia wasn’t one of them. The 10 stocks that made the cut could produce monster returns in the coming years. <\/p>\n Consider when Netflix made this list on December 17, 2004… if you invested $1,000 at the time of our recommendation, you’d have $355,124!* Or when Nvidia made this list on April 15, 2005… if you invested $1,000 at the time of our recommendation, you’d have $1,296,254!* <\/p>\n Now, it’s worth noting Stock Advisor’s total average return is 901% \u2014 a market-crushing outperformance compared to 210% for the S&P 500. Don’t miss the latest top 10 list, available with Stock Advisor, and join an investing community built by individual investors for individual investors. <\/p>\n See the 10 stocks \u00bb<\/a> <\/p>\n *Stock Advisor returns as of October 26, 2026. <\/p>\n Patrick Sanders<\/a> has positions in Nvidia. The Motley Fool has positions in and recommends Nvidia. The Motley Fool has a disclosure policy<\/a>. <\/p>\n<\/a> Nvidia CEO Jensen Huang. Image source: Getty Images. Nvidia is getting more valuable with every generation <\/p>\n