‘I'm not sure yet whether I'm going to regret this or not,' said Nvidia CEO Jensen Huang as he revealed 2026 plans for the company’s Rubin GPU platform.
Nvidia CEO Jensen Huang disclosed the company’s next-generation AI-accelerating GPU platform, set for release in 2026, during his keynote address at Computex as the massive tradeshow wrapped up earlier this month.
The current generation on the market is Hopper, and the next generation, due at the end of this year, is Blackwell. Next year, we’ll see Blackwell Ultra with its performance boost for AI architecture. Come 2026, we’ll see the Rubin architecture.
Nvidia product naming conventions are taken from famous scientists. This new architecture is named after Vera Rubin, an astronomer.
Huang didn’t spend a lot of time talking about Rubin. And, in fact, he questioned whether he ought to announce it. “I’m not sure yet whether I’m going to regret this or not,” he told the audience. His hesitation could be due to the so-called Osborne effect, whereby a company’s announcement of the next iteration of a tech product causes customers to halt sales on the current product.
Here’s what we do know: The Rubin AI platform will use HBM4 memory (which is not even out yet) and NVLink 6 Switch, operating at 3,600 Gbps. He also introduced a new ARM-based CPU called Vera, which will be a part of a new accelerator board called Vera Rubin, just like the Grace Hopper chips combine a Grace CPU and Hopper GPU.
Robotics is the next generation of AI
While Huang didn’t go into deep technical details about the processor, he did make a bold prediction that the next wave of AI would be what he described as “physical AI,” or AI that understands the laws of physics and can work among us. “One day, everything that moves will be autonomous,” he said.
To do so, physical AI has to understand the world model, so that they understand how to interpret the world, how to perceive the world, he said. They have to have excellent cognitive capabilities so they can understand us, understand what we asked, and perform the tasks in the future.
Huang went on to predict a robotic future. “All of the factories will be robotic. The factories will orchestrate robots, and those robots will be building products that are robotic. Robots interacting with robots, building products that are robotic. Well, in order for us to do that, we need to make some breakthroughs,” he said before showing off a demo video of researchers developing robots powered by physical AI.
Physical AIs use multimodal LLMs that enable robots to learn, perceive and understand the world around them and plan how they’ll act. One of the integral technologies for advancing robotics is reinforcement learning, gained from human feedback to learn particular skills.
In this vision, however, generative physical AI can learn skills using reinforcement learning from feedback in a simulated world rather than from humans. “These simulation environments are where robots learn to make decisions by performing actions in a virtual world that obeys the laws of physics. In these robot gyms, a robot can learn to perform complex and dynamic tasks safely and quickly, refining their skills through millions of acts of trial and error,” Huang said.
Annual upgrades for IA accelerators
The news of a new architecture isn’t exactly a surprise, as Nvidia recently announced it wants to go to a one-year cadence for its new architectures rather than two years. It’s extremely ambitious and leaves no room for error to update GPU technology with billions of transistors already. AMD has said it will go to a similar cadence with its Instinct line.
“Our company has a one-year rhythm. Our basic philosophy is very simple: build the entire data center scale, disaggregate and sell to you parts on a one-year rhythm, and push everything to technology limits,” Huang said.
It’s the “sell you the parts” approach that is the reason Nvidia stock is soaring, its market value has surpassed $3 trillion, and Huang’s estimated net worth has surpassed $100 billion.
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