NVIDIA Unveils Rubin and Open Models at CES 2026

Jensen Huang, founder and CEO of NVIDIA, took the stage at the Fontainebleau Las Vegas to open CES 2026 with a clear thesis: artificial intelligence is scaling into every domain and every device. At the center of his remarks were two announcements: Rubin, the company's first six-chip AI platform, now in full production, and Alpamayo, a family of open models for autonomous vehicle development.

"Computing has been fundamentally reshaped as a result of accelerated computing, as a result of artificial intelligence," Huang said. By his estimate, some $10 trillion in computing accumulated over the last decade is now being modernized toward this new way of computing. It's the figure NVIDIA wants to use to frame the size of the market it says it's transforming.

Rubin: Cutting the Cost of Generating Tokens

The new platform's name honors American astronomer Vera Rubin. It succeeds the Blackwell architecture and, according to Huang, is the first "extreme-codesigned" AI platform with six chips, now in full production.

NVIDIA's technical argument is that scaling AI to what it calls "gigascale" requires designing every component at once — chips, trays, racks, networking, storage and software — to eliminate bottlenecks. That's what the company means by extreme codesign: not optimizing individual pieces, but the entire system at once.

Rubin's components, built "from the data center outward," include:

• Rubin GPUs with 50 petaflops of NVFP4 inference.
• Vera CPUs, engineered for data movement and agentic processing.
• NVLink 6 scale-up networking.
• Spectrum-X Ethernet Photonics scale-out networking.
• ConnectX-9 SuperNICs.
• BlueField-4 DPUs.

The figure NVIDIA wants the industry to remember isn't about raw performance — it's about cost: Rubin promises to deliver AI tokens at one-tenth the cost of the previous platform. At a time when inference spending — the process of running an already-trained model to answer users — is the line item worrying companies deploying AI at scale the most, cutting costs tenfold is the underlying sales pitch.

"The faster you train AI models, the faster you can get the next frontier out to the world," Huang said. "This is your time to market. This is technology leadership."

Huang also unveiled "AI-native" storage, the NVIDIA Inference Context Memory Storage Platform, a KV-cache layer built for long-context inference. The company credits it with five times more tokens per second, five times better performance per dollar of total cost of ownership, and five times better energy efficiency.

Open Models as Strategy

The second pillar of the keynote is that NVIDIA doesn't present itself merely as a hardware maker, but as a builder of frontier models — and it does so, according to Huang, in the open.

"Now on top of this platform, NVIDIA is a frontier AI model builder, and we build it in a very special way. We build it completely in the open so that we can enable every company, every industry, every country, to be part of this AI revolution," he said.

The portfolio spans six domains: Clara for healthcare, Earth-2 for climate science, Nemotron for reasoning and multimodal AI, Cosmos for robotics and simulation, GR00T for embodied intelligence, and Alpamayo for autonomous driving. All of them, according to the company, trained on its own supercomputers.

"Every single six months, a new model is emerging, and these models are getting smarter and smarter," Huang said, attributing the explosion in download numbers to that cadence.

The bet on open source carries an obvious strategic reading: if the models being downloaded and run at massive scale are trained and optimized on NVIDIA hardware, the company strengthens its position even without charging for the model itself. Open software drives sales of the hardware that runs it.

Personal AI: Agents Leave the Cloud

Huang insisted that the future of AI isn't just about supercomputers — it's personal too. He showed a demo of a personalized AI agent running locally on the DGX Spark desktop supercomputer, embodied in a Reachy Mini robot using Hugging Face models.

The idea he wanted to convey was the combination of open models, model routing and local execution turning agents into "physical collaborators." "The amazing thing is that is utterly trivial now, but yet, just a couple of years ago, that would have been impossible, absolutely unimaginable," he said.

On the enterprise side, Huang cited companies integrating NVIDIA AI into their products, including Palantir, ServiceNow, Snowflake, CodeRabbit, CrowdStrike and NetApp. "Whether it's Palantir or ServiceNow or Snowflake — and many other companies that we're working with — the agentic system is the interface," he said, summing up his vision that interacting with software will increasingly happen through agents rather than menus and buttons.

On DGX Spark itself, NVIDIA announced at CES up to 2.6 times more performance for large models, support for Lightricks' LTX-2 and FLUX image models, and upcoming availability of NVIDIA AI Enterprise.

Physical AI: From Simulator to the Road

The most concrete part of the announcement is the one that brings AI down to the physical world. NVIDIA's strategy involves training systems with synthetic data in virtual worlds before they interact with reality.

Huang showed the open world foundation models Cosmos, trained on video, robotics data and simulation. Cosmos generates realistic videos from a single image, synthesizes multi-camera driving scenarios, models edge-case situations from prompts, and performs physical reasoning and trajectory prediction.

The central announcement of this section was Alpamayo, an open portfolio of vision-language-action models, simulation blueprints and datasets aimed at level 4 autonomy — the degree at which a vehicle can drive itself under defined conditions without human intervention. It includes Alpamayo R1, presented as the first open reasoning model of its kind for autonomous driving, and AlpaSim, an open simulation blueprint for vehicle testing.

"It doesn't just take sensor input and activate the steering wheel, brakes and accelerator — it also reasons about the action it's about to take," Huang explained, before screening a video of a vehicle driving through San Francisco traffic.

The concreteness comes with a manufacturer: the first passenger car with Alpamayo, built on the NVIDIA DRIVE platform, will soon hit the road in the new Mercedes-Benz CLA. Huang placed the arrival of this "AI-defined driving" in the United States this year, after the CLA recently earned a five-star EuroNCAP rating.

The keynote also leaned on the DRIVE Hyperion platform, described as open, modular and level-4-ready, and on a robotics ecosystem Huang illustrated with robots trained in the simulated Isaac Sim and Isaac Lab environments, alongside partners such as Synopsys, Cadence, Boston Dynamics and Franka. He was joined onstage by Siemens CEO Roland Busch to announce an expansion of their industrial software alliance.

"These manufacturing plants are essentially going to be giant robots," Huang said. And about the car: "Our vision is that someday every car, every truck, will be autonomous, and we're working toward that future."

What to Watch From Here

NVIDIA's script at this CES is consistent with its position: it sells a full platform — hardware, networking, storage and models — and presents open source as the lever that turns that stack into the standard everyone builds on. The promise of cutting the cost of generating tokens tenfold is the number companies will actually measure, because it determines whether deploying AI at scale stops being prohibitively expensive.

In autonomous driving, the most verifiable commitment is the Mercedes-Benz CLA with Alpamayo hitting American roads this year. It's the kind of announcement that verifies itself: either there are cars on the road or there aren't. The rest — the six-month model cadence, level 4 autonomy, factories turned into "giant robots" — is the roadmap Huang sketched out for the coming years.