Nvidia GTC 2026 keynote live blog — Vera Rubin GPUs and CPUs, DLSS 5, and the ‘future of technology’

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Well, we’re done now, I guess. Nvidia closed out its keynote with an animation of several robots (plus Jensen) sitting around a fire singing a song about the keynote. It’s a country song, and probably generated by AI? I don’t really know what to say about this one. A rough-talking robot singing about tokens and open-source software wasn’t on my bingo card.

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In a not-at-all-awkward meeting, Olaf from Frozen joins Jensen on stage. The executive is now describing the various AI models used to make Olaf, which is… something. Anyway, Olaf is helping close out the keynote.

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Nvidia is showing off 110 robots at GTC, showcasing its “physical AI.” Nvidia announced several new partners, including four new partners for robo-taxis, including BYD, Hyundai, and Nissian. Nvidia is also partnering with Uber, connecting robo-taxis into Uber’s network in select cities.

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Nvidia says that Nemotron 3 Ultra will be the best base model in the world. In order to scale out Nemotron, Nvidia is creating a coalition for Nemotron 4, including companies like Black Forest Labs, Perplexity, Mistral, and Cursor.

NemoClaw is enterprise-secure, helping protect sensitive information. AI agents can communicate externally and execute without intervention, which is obviously a problem. NemoClaw provides a reference software stack for businesses to keep OpenClaw secure.

Now Jensen is describing what OpenClaw is, which is a description no one in the room at GTC actually needs. For anyone who isn’t aware, it’s an agent that can connect to cloud systems. It can spawn other agents, do scheduling, decompose a problem, etc. Jensen says it’s an operating system. “It’s no different than how Windows allowed us to make personal computers.”

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Nvidia is streamlining the process of setting up an AI agent with OpenClaw. Type two lines of shell commands, and you’re off to the races with an AI agent. From there, Nvidia says you just need to give it a task and let the agent run its course.

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Nvidia is working on a system called Vera Rubin Space-1, which will be the first data center in space. Sounds like we’re in early stages, but Nvidia has “a lot of great engineers” working on it.

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Nvidia built Omniverse to meet suppliers virtually, allowing co-design in the data center at a much broader scale. The goal is to leave “no power squandered.” They’re blueprints for AI factories, which Nvidia calls its DSX platform.

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Jensen is teasing next-gen Feynman systems. It has a new GPU, new LPU, new CPU called Rosa, Bluefield 5, and Kyber with copper and CPO scale up. Feynman systems are on-track for 2028, so we’ll hear a lot more about them throughout the year. At next year’s GTC, we’ll probably run back the same talking point with Feynman that we hard about with Vera Rubin this year.

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Vera Rubin is undoubtedly Nvidia’s most ambitious system to date, featuring seven chips across five rack systems. Compared to x86 and Hopper, Nvidia says Vera Rubin is able to deliver 700 million tokens per second compared to just 2 million

Jensen admits that the Grace Blackwell sampling had some issues, but apparently Vera Rubin sampling is going smoothly. In fact, the first Vera Rubin is system is apparently already running in Microsoft’s Azure Cloud.

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Groq is important for Nvidia because it pushes beyond the limits of NVL72. With Groq LPX, Nvidia says it’s able to deliver up to 10x in revenue to companies using Vera Rubin. It helps solve the problem of delivering low latency and high throughput, which Jensen described as “enemies of each other.”

Nvidia combined one chip for high throughput and one for low latency, which it achieved with disaggregated inference.

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Tokens are “the new commodity,” according to Nvidia. For businesses, Nvidia says that the throughput of an AI factory at iso power is something that will be “studied for years.” More tokens means smarter models, and the smarter the models get, you need better token throughput. Nvidia says that, at every tier, Vera Rubin delivers much higher throughput.

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Jensen explains how NVLink for Rubin Ultra works, with compute sitting in the front and the scale-up fabric in the back.

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Vera Rubin NVL72 is the “engine supercharging the era of agentic AI.” A new addition is the Groq 3 LPX tray, and as a whole, Nvidia says it’s delivered 40 million times more compute over the past decade. Jensen is showing off Vera Rubin on stage; the whole thing. It’s “one giant system.”

Jensen says the Vera CPU is designed for high single-threaded performance, and the company built it go along with its racks for agentic processing.

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Jensen says data centers used to be a place to store files, and they’re now a factory to generate tokens. Inference is the workload and tokens are the new commodity, says Nvidia. Now, onto a short video showing how we got here.

“Nobody believed me,” says Jensen. When Nvidia says it delivered 30x better performance per watt, on NVL72, it was wrong. Apparently, it delivers 50x better performance per watt. Jensen once again goes back to Moore’s Law, saying it would maybe deliver 1.5x perf in a year.

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Nvidia NVL72 was a “giant bet,” says Jensen, and he thanked Nvidia’s partners for sticking with the company. “It wasn’t easy for anybody… inference is the ultimate hard.” The bet paid off, according to Nvidia, which you can see in the slide above.

Jensen says Nvidia is the only company that runs every domain of AI across every domain of AI models. Between Nvidia, Anthropic, and Meta SL, Jensen says that represents a third of the world’s AI compute. Nvidia says it’s proven that “you can build with complete confidence” as an AI infrastructure company.

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Last year, Nvidia said it saw about $500 billion of high confidence demand and purchase orders for Blackwell and Rubin through 2026. “I see through 2027 at least $1 trillion,” says Jensen. “Now, does it make any sense?” Jensen says that’s what he’s going to spend the rest of the keynote talking about.

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There’s been rapid AI development over the past few years. In 2023, it was ChatGPT. In 2024, it was reasoning models like o1, and in 2025, it was huge models with massive context windows like Claude Code. It’s the first “agentic model,” says Jensen. The executive says 100% of Nvidia is using Claude Code, along with other models. In 2026, Nvidia says we’ve reached an “inflection point for inference.”

Jensen is talking about some of the many “AI native” companies, which are only possible because Nvidia “reinvented computing,” says the executive. We’re at the beginning of a new platform shift, he says, and it’s akin to the PC revolution. This really kicked off over the past two years with ChatGPT kicking off the generative AI era.

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Nvidia says cuDNN, or CUDA Deep Neural Network, is one of the most important libraries the company has ever made, saying it caused the “big bang” of modern AI. Nvidia is showing a short video about its various CUDA-X libraries, including a life-like video that’s entirely simulated.

“We are an algorithm company,” says Jensen, after spending 10 minutes talking about the applications of Nvidia’s software stack in just about every industry. Everything comes back to Nvidia’s CUDA-X libraries, and Jensen describes them as the “crown jewel” of the company.

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AI has a lot of applications, but Jensen says it isn’t as simple as throwing GenAI at the wall and hoping it sticks. “We have to have domain-specific libraries that solve problems in every one of these verticals,” he says.

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Jensen describes Nvidia as “vertically integrated but horizontally open,” which may or may not raise some eyebrows at the FTC. Regardless, Nvidia says there’s “no other way” it can be given what it’s trying to do with accelerated computing, delivering the entire stack to customers.

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“As you know, [OpenAI] is completely compute-constrained.” Jensen says that OpenAI will come to AWS this year, hopefully lightening the load on its massive infrastructure demand.

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Jensen likes to talk about the death of Moore’s Law, and he’s doing so once again. “Moore’s Law has run out of steam, accelerated computing allows us to take giant leaps forward.” Jensen is showing off an example with Google Cloud and showing how Nvidia’s acceleration can be repeated across companies and industries.

Jensen is describing the importance of AI in unstructured data. He says this data makes up 90% of the world’s data but it’s been “useless” because you can’t search or query it. IBM, the inventor of SQL, is accelerating WatsonX data with the cuDF acceleration framework.

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Jensen jokes that he’s going to spend the rest of the keynote going through the slide you can see above about structured data. This is “the ground truth” of enterprise computing.

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Nvidia says it combined controllable 3D graphics and structured data with generative worlds. “This concept of fusing structured data with generative AI will repeat itself in one industry after another industry after another industry.”

The first announcement is a big one: DLSS 5. Nvidia is showing it off in Resident Evil: Requiem, Hogwarts Legacy, and Starfield. We’re all waiting eagerly to hear more details about what DLSS 5 includes, but the side-by-side comparisons are compelling.

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Jensen says that “GeForce is Nvidia’s greatest marketing campaign.” It’s an interesting way to frame the conversation, and one that Nvidia has been trying to crack for the past few years. Jensen paints a picture of Nvidia creating the first programmable shader 25 years ago, which eventually led to CUDA, and used GeForce as a vehicle to drive adoption.

The prevalence of CUDA has accelerated what Nvidia calls a “flywheel.” Nvidia attracts developers who develop on CUDA, which leads to more people adopting Nvidia hardware, and the lifecycle continues. Because of this, Jensen says the price of GPUs using the now-dated Ampere architecture has actually gone up in the cloud.

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CUDA is one of the major reasons Nvidia is in the position it’s in today, and this GTC marks the 20th anniversary of CUDA. “The single hardest thing is to have built up our install base, we’re in every cloud and computer company in every single industry,” says Jensen.

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Jensen Huang has taken the stage in a familiar leather jacket. Sorry folks, there’s no special jacket this time around. Jensen is starting off the show thanking some of the people that hosted the preshow leading up to the keynote.

We may have started a few minutes late, but the keynote has officially begun. We begin with a short video about AI tokens and all the wonderful things (according to Nvidia) we’ve all done with them, from healthcare to space to construction.

We’re all sitting in surprise here at Tom’s Hardware at the amount of country music playing before Jensen takes the stage. We’re nearly a quarter past the top of the hour at this point and still waiting for the keynote to start. There’s nothing wrong with country music, but rustic Americana and enterprise AI isn’t a combo I’d normally expect.

We’re a few minutes past the top of the hour, and we’re still waiting on the keynote to start. In the meantime, a quick reminder that you can watch along with us through the live stream above.

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Run to the bathroom, get your drink ready, and settle in. We’re just a few minutes away from the start of GTC 2026. Jensen will probably start with a short history of Nvidia’s role in AI, but we expect the announcements to rapid-fire out after that point. We’re sat down in the SAP Center in San Jose and ready to dig in.

There’s always room for surprises, especially at Nvidia’s own GTC event, but there are a few key announcements we’re focused in on:

Hello and welcome to Tom’s Hardware’s live blog for the GTC 2026 keynote. Jensen Huang is moments away from taking the stage. Myself, Jake Roach, will be tending the blog while our very own Paul Alcorn and Jeffrey Kampman are on the ground to cover all of the announcements in real-time.