Nvidia Corp. Chief Executive Officer Jensen Huang said that the company’s highly anticipated Rubin data center processors are in production and customers will soon be able to try out the technology.
All six of the chips for a new generation of computing equipment — named after astronomer Vera Rubin — are back from manufacturing partners and on track for deployment by customers in the second half of the year, Huang said at the CES trade show in Las Vegas Monday.
“Demand is really high,” he said. The growing complexity and uptake of artificial intelligence software is placing a strain on existing computer resources, creating the need for much more, Huang said.
Shares rose 2% to $191.86 in New York on Tuesday. They have risen about 28% over the past year.
Nvidia, based in Santa Clara, California, is seeking to maintain its edge as the leading maker of artificial intelligence accelerators, the chips used by data center operators to develop and run AI models.
Some on Wall Street have expressed concern that competition is mounting for Nvidia — and that AI spending can’t continue at its current pace. Data center operators also are developing their own AI accelerators. But Nvidia has maintained bullish long-term forecasts that point to a total market in the trillions of dollars.
Rubin is Nvidia’s latest accelerator and is 3.5 times better at training and five times better at running AI software than its predecessor, Blackwell, the company said. A new central processing unit has 88 cores — the key data-crunching elements — and provides twice the performance of the component that it’s replacing.
The company is giving details of its new products earlier in the year than it typically does — part of a push to keep the industry hooked on its hardware, which has underpinned an explosion in AI use. Nvidia usually dives into product details at its spring GTC event in San Jose, California.
Even while talking up new offerings, Nvidia said previous generations of products are still performing well. The company also has seen strong demand from customers in China for the H200 chip that the Trump administration has said it will consider letting the chipmaker ship to that country.
License applications have been submitted, and the US government is deciding what it wants to do with them, Chief Financial Officer Colette Kress told analysts. Regardless of the level of license approval, Kress said, Nvidia has enough supply to serve customers in the Asian nation without affecting the company’s ability to ship to customers elsewhere in the world.
For Huang, CES is yet another stop on his marathon run of appearances at events, where he’s announced products, tie-ups and investments all aimed at adding momentum to the deployment of AI systems. His counterpart at Nvidia’s closest rival, Advanced Micro Devices Inc.’s Lisa Su, was slated to give a keynote presentation at the show later Monday.
The new hardware, which also includes networking and connectivity components, will be part of its DGX SuperPod supercomputer while also being available as individual products for customers to use in a more modular way. The step-up in performance is needed because AI has shifted to more specialized networks of models that not only sift through massive amounts of inputs but need to solve particular problems through multistage processes.
The company emphasized that Rubin-based systems will be cheaper to run than Blackwell versions because they’ll return the same results using smaller numbers of components. Microsoft Corp. and other large providers of remote computing will be among the first to deploy the new hardware in the second half of the year, Nvidia said.
For now, the majority of spending on Nvidia-based computers is coming from the capital expenditure budgets of a handful of customers, including Microsoft, Alphabet Inc.’s Google Cloud and Amazon.com Inc.’s AWS. Nvidia is pushing software and hardware aimed at broadening the adoption of AI across the economy, including robotics, health care and heavy industry.
As part of that effort, Nvidia announced a group of tools designed to accelerate development of autonomous vehicles and robots.
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