Nvidia Wants to Own Every Chip Inside AI Data Centers

What happened

Nvidia is hyping up its new Vera Rubin chip system this week, revealing new performance benchmarks for the GPU and CPU combo ahead of rival AMD’s annual product event in San Francisco on Thursday. During a lengthy technical workshop last week at the company’s headquarters in Santa Clara, California, Nvidia executives boasted to a small group of journalists about the chip system’s increased power and efficiency capabilities.

The biggest takeaway: Nvidia, which has long specialized in making GPUs, is increasingly trying to position itself as a supplier of CPUs that can power AI agents. While GPUs are still the main hardware that companies use to train and run their AI models, the industry's shift toward more complex, agentic systems has increased demand for CPUs, which can orchestrate data flows, networking, and other software tasks.

That's likely one reason Nvidia has been eager to promote itself as a supplier of complete AI systems rather than just AI chips. Vera Rubin is Nvidia’s successor to its hybrid superchip system Grace Blackwell and represents the linchpin of its near-term future powering the AI industry. It’s designed to offer one CPU for every two GPUs.

In a single Vera Rubin NVL 72 super chip system, there are 36 Vera CPUs for every 72 Rubin GPUs. Nvidia is also selling the Vera CPU as a stand-alone product, and it has reportedly told Chinese customers these could be ready as soon as August. The CPU chip Nvidia is using for its new Vera Rubin hardware system.

Nvidia executives emphasized that its new Vera Rubin NVL72 racks—a stack of chips packed into a single liquid-cooled platform—are much more “plug-and-play” than some of its earlier products. During a brief tour of a Nvidia data center lab in Silicon Valley, Nvidia executives shared that OpenAI already has one Vera Rubin rack in use.

Nvidia CEO Jensen Huang didn’t make an appearance at the workshop in Santa Clara last week; he was in Japan announcing the chipmaker’s new partnerships with a number of Japanese firms to develop AI for robotics. The briefings were instead led by Ian Buck, Nvidia’s longtime vice president of accelerated computing and the architect behind the company’s CUDA software.

“We’re on a road map to crank out new architectures, not just GPUs but CPUs,” Buck told reporters. ” The meetings were held in Huang’s executive briefing center, where multiple desks nearby were piled with bags of Taiwanese snacks that the CEO brought back from his recent trip to Computex, a massive annual semiconductor trade show in Taipei, an Nvidia spokesperson told WIRED.

A rack containing Nvidia’s Vera CPUs, which are designed to pair with the company’s next-generation Rubin GPUs in AI data centers. Nvidia claims that the Vera Rubin NVL72 system will process 10 times as many tokens per watt as the company’s Grace Blackwell super chip.

Why it matters

” This means customers can theoretically reduce the amount of time it takes to install each rack from a couple of hours to a few minutes, a point that was brought up by both Buck and Andrew Bell, Nvidia’s senior vice president of hardware engineering. And the new chip system is 100 percent liquid-cooled, which can reduce the amount of energy needed to cool the c

What to watch

The company says that its Vera CPU is also faster at processing agentic AI tasks compared to rival CPUs from AMD and Intel (though the tests it ran to support those benchmarks appears to have used slightly older generations of its competitors’ CPUs).

Localized memory subsystems on the new chips will also offer nearly three times as much memory bandwidth as Blackwell, which will likely be an appealing feature to many companies amid an ongoing shortage of high-bandwidth memory.