Whichever AI segment you follow most closely, be it healthcare or any other, you had to pause to absorb this week’s financial report from Nvidia.
Wednesday the chipmaker reported a 101% blast in second-quarter revenues versus the same period last year. The dollar figure, $13.5 billion, also represents a jump of 88% over the first quarter. And the company netted income of $6.18 billion—nine times what it made in Q2 2022.
What primed the pump for the extraordinary performance? What else? The race to adopt generative AI. With that has come pedal-to-the-metal demand for high-end, AI-ready GPUs of the kind Nvidia makes.
“A new computing era has begun,” Nvidia founder and CEO Jensen Huang told the press Aug. 23, two months after his company became the seventh in the U.S. to hit $1 trillion in market capitalization. “Leading enterprise IT system and software providers have announced partnerships to bring Nvidia AI to every industry.”
Notably, the boffo success owes much to chip sales at data centers, where algorithm training takes place. Q2 revenues from that part of the company’s business soared 141% from the previous quarter and 171% from a year ago, to $10.3 billion.
Technology market watchers have met the development with almost as much giddiness as investors themselves. Here’s a taste.
- Nvidia is now a bellwether for the AI boom, and an ecosystem has emerged in which a win for Nvidia can come in tandem with wins for some additional companies—as well as questions for others.—Yahoo! Finance
- Nvidia CEO Jensen Huang went out of his way to talk about [preferred customer] VMware when answering AI questions from analysts. He didn’t have to do this, but he did.—Insider
- Competition doesn’t look like an immediate concern. Nvidia executives emphasized the move toward GPUs to power AI tools and away from general-purpose processors. Nvidia is estimated to have around a 90% market share for AI-related GPUs currently.—Barron’s
- Until recently, Nvidia got the biggest share of its revenue from sales of GPUs for rendering images in video games. But AI researchers started using those chips in 2012 for tasks such as machine learning, a trend that Nvidia exploited over the years by adding enhancements to its GPUs and many pieces of software to reduce labor for AI programmers.—The New York Times
- Demand for GPUs in AI applications is huge … Huang’s move to buy back [$25 billion in] stock when it is more expensive than ever is risky, but it shows his confidence in Nvidia’s continued success.—Ars Technica
Lots more coverage here.