Ask anyone what Nvidia makes, and they’re likely to first say “GPUs.” For decades, the chipmaker has been defined by advanced parallel computing, and the emergence of generative AI and the resulting surge in demand for GPUs has been a boon for the company.
But Nvidia’s recent moves signal that it’s looking to lock in more customers at the less compute-intensive end of the AI market—customers who don’t necessarily need the beefiest, most powerful GPUs to train AI models, but instead are looking for the most efficient ways to run agentic AI software. Nvidia recently spent billions to license technology from a chip startup focused on low-latency AI computing, and it also started selling stand-alone CPUs as part of its latest superchip system.
Yesterday, Nvidia and Meta announced that the social media giant had agreed to buy billions of dollars’ worth of Nvidia chips to provide computing power for its massive infrastructure projects—with Nvidia’s CPUs as part of the deal.
The multiyear deal is an expansion of a cozy ongoing partnership between the two companies. Meta previously estimated that by the end of 2024, it would have purchased 350,000 H100 chips from Nvidia, and that by the end of 2025 the company would have access to 1.3 million GPUs in total (though it wasn’t clear whether those would all be Nvidia chips).
As part of the latest announcement, Nvidia said that Meta would “build hyperscale data centers optimized for both training and inference in support of the company’s long-term AI infrastructure roadmap.” This includes a “large-scale deployment” of Nvidia’s CPUs and “millions of Nvidia Blackwell and Rubin GPUs.”
Notably, Meta is the first tech giant to announce it was making a large-scale purchase of Nvidia’s Grace CPU as a stand-alone chip, something Nvidia said would be an option when it revealed the full specs of its new Vera Rubin superchip in January. Nvidia has also been emphasizing that it offers technology that connects various chips, as part of its “soup-to-nuts approach” to compute power, as one analyst puts it.
Ben Bajarin, CEO and principal analyst at the tech market research firm Creative Strategies, says the move signaled that Nvidia recognizes that a growing range of AI software now needs to run on CPUs, much in the same way that conventional cloud applications do. “The reason why the industry is so bullish on CPUs within data centers right now is agentic AI, which puts new demands on general-purpose CPU architectures,” he says.
A recent report from the chip newsletter Semianalysis underscored this point. Analysts noted that CPU usage is accelerating to support AI training and inference, citing one of Microsoft’s data centers for OpenAI as an example, where “tens of thousands of CPUs are now needed to process and manage the petabytes of data generated by the GPUs, a use case that wouldn’t have otherwise been required without AI.”
Bajarin notes, though, that CPUs are still just one component of the most advanced AI hardware systems. The number of GPUs Meta is purchasing from Nvidia still outnumbers the CPUs.
“If you’re one of the hyperscalers, you’re not going to be running all of your inference computing on CPUs,” Bajarin says. “You just need whatever software you’re running to be fast enough on the CPU to interact with the GPU architecture that’s actually the driving force of that computing. Otherwise, the CPU becomes a bottleneck.”
Meta declined to comment on its expanded deal with Nvidia. During a recent earnings call, the social media giant said that it planned to dramatically increase its spending on AI infrastructure this year to between $115 billion and $135 billion, up from $72.2 billion last year.
