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Nvidia’s stock soared nearly 30% after it announced its first-quarter financial results yesterday, setting the stage for Nvidia to become only the fifth publicly traded U.S. company to be currently worth $1 trillion — joining Apple, Microsoft, Alphabet and Amazon. And it’s all thanks to the hunger for high-powered AI chips in the era of generative AI.
This was a predictable outcome: Back in February, VentureBeat took a deep dive into how Nvidia has come full circle when it comes to AI. Its hardware and software helped power the deep learning revolution, which in turn drove the need for more and more GPUs as large language models like GPT have grown bigger and more powerful.
Today’s massive generative AI models require thousands of GPUs to run — and Nvidia holds about 88% of the GPU market, according to Jon Peddie Research. In fact, OpenAI reportedly used 10,000 Nvidia GPUs to train ChatGPT, while Elon Musk is rumored to have purchased thousands of GPUs for his new AI company X.ai.
Generative AI was a ‘flashpoint’ for Nvidia fortunes
Yesterday, Nvidia forecast second-quarter revenue more than 50% above Wall Street estimates. CEO Jensen Huang told CNBC that “the flashpoint was generative AI … we know that CPU scaling has slowed, we know that accelerated computing is the path forward, and then the killer app showed up.”
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But Nvidia’s good fortune has also led to bad luck for many companies seeking access to GPU compute. Some have even called it a crisis for all but the most deep-pocketed of AI-focused companies.
“The closest analogy is, I’ve said what’s happening right now is a little bit like Game of Thrones,” said David Katz, partner at Toronto’s Radical Ventures. “There’s this insatiable appetite for compute that’s required in order to run these models and large models.”
No one is catching Nvidia right now, thanks to its platform approach
No one will catch up and topple Nvidia off its AI perch anytime soon, say experts.
These days, Nvidia is synonymous with AI, Gartner analyst Chirag Dekate told VentureBeat in February.
“It is not just a GPU computing company, it’s basically an AI supercomputing company,” he explained. “Nvidia has had complete freedom of the AI landscape, they have taken advantage of really sharp, business-savvy investments and focused approaches that basically enable them to dominate their market.”
Nvidia won’t have free rein forever — chip competitors like AMD and Google are nipping at its heels, for one thing, while geopolitical forces hover ominously. (With the United States’ latest chip export control, state-of-the-art GPUs like Nvidia’s A100 and H100 can no longer be sold to China, for example.)
But Nvidia’s famed platform strategy and software-focused approach are still very, very hard to beat, experts say.
“While other players offer chips and/or systems, Nvidia has built a strong ecosystem that includes the chips, associated hardware and a full stable of software and development systems that are optimized for their chips and systems,” analyst Jack Gold wrote for VentureBeat last September.
Nathan Benaich, founder and general partner of Air Street Capital, pointed out that Nvidia has also been “very nimble” with integrating new capabilities into its system. Other AI chip startups have under-invested in software tooling, so while they have created cloud computing platforms that may be faster or cheaper than Nvidia’s, they “don’t come with a commensurate improvement in the current programming experience.”
Ultimately, he told VentureBeat, the AI game “is Nvidia’s to lose.”
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