Inside Nemotron & NVIDIA’s AI Lab | Bryan Catanzaro
episodePreviously titled “Why NVIDIA Is Giving Away AI Models | Bryan Catanzaro” — renamed by the publisher on Aug 2, 2026
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What is the main focus of the conversation with Bryan Catanzaro?
If you accept as the truth that we're going to be running at the limit, then what that means is that the way to get more intelligence is to be more efficient. We can't get more intelligence by applying more force if we're already at the limit. We have to be more thoughtful about how we use what we have. We build tools, we build external organs that help us solve problems. You know, we We have an external stomach, we call it kitchen. Now we're creating an external brain. What is the implications of an external brain? Pretty profound. Nobody actually really knows. Hi, I'm Met Turk.
Welcome back to the Mad Podcast. Open source AI is having yet another moment with powerful new models arriving almost weekly, and my guest today is one of the very best people to unpack it all. Brian Catenzaro leads Nimotron, Nvidia's family of open foundation models. Now, not everyone realizes Nvidia has a massive effort to build frontier AI models, but it employs hundreds of AI researchers and Nimotron 3 Ultra immediate. Became the number one US open weights model when it was released just a couple weeks ago. We begin this conversation with the state of open source AI and the race between the US and China. And then we go deep inside Nimotron. 4-bit training, hybrid member transformer architecture, mixture of experts, multi-token prediction, and multi-teacher distillation, all in plain language.
And finally, we get a rare look at how a modern AI research organization actually.
How is open‑source AI reshaping the frontier of large language models?
Runts, how you get many brilliant minds to build one model instead of a hundred papers. Please enjoy this awesome conversation with Brian Katantaro. Alright, Brian, excited to do this. It seems that open source is having a banner. So you guys at NVIDIA just released Nimotron 3 Ultra, which is uh an important moment and the best open source, open weight model in the US. And that was just a few days ago. And then uh even more recently, GLM 5.2 came out, and that was another moment. So it seems that things are accelerating. accelerating in open source AI. It feels like a great uh place to start. What's your assessment uh about where we are and how wide the gap between closed source and open source currently is?
Well, it's really exciting to see all of the energy going into open technologies for AI because we know that um open technologies make it possible for people to innovate. You know, the internet is such a great example of that. Um, we actually did have closed internets. I don't know if you remember things like America Online and Prodigy back in the day. Um, and they were great. Um and open internet has also uh been amazing, right? Like so many different companies have been able to figure out how to transform their work. Um, thanks to uh an open technology. The application of the internet to retail is very different from the application of the internet to healthcare or manufacturing, but all of them have been totally transformed um by the internet.
Um AI uh I believe is uh also a very transformational technology and also a technology that needs to be applied in very diverse ways. And because of that, I believe that open technologies for AI are really fundamental. Um and it's very exciting to see continued um investment and development of open technologies from uh for AI from so many different organizations around the world. Um uh and uh you know I I hope that that continues.
And what's your sense for how far behind open source is compared to closed source? It's been the the big trend of the last few years has been this sort of narrowing gap. Do you do you think that open source is almost there or the bar keeps uh getting raised by the closed source models?
Well, I I feel like this question, um uh it it's maybe a tempting question because it you know, it's fun to set up kind of competition, but but I actually feel like the whole AI community is moving very fast. Um and if you look, for example, at the progress in AI, whether it's closed or open, just over the past three months, it's been incredible. Um and so if you're in a field that's moving really, really fast, I think that's more important than any particular gaps that might exist between different models.
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Chapters
8 chapters
1
What is the main focus of the conversation with Bryan Catanzaro?
0:00–1:23
2
How is open‑source AI reshaping the frontier of large language models?
1:23–5:29
3
Why are closed‑source labs slowing down the progress of open AI?
5:29–11:17
4
Is the United States falling behind China in open‑model development?
11:17–16:33
5
Why do companies choose to adopt open models instead of building their own?
16:33–24:01
6
What was the 2008 “crazy” bet on GPUs for machine learning?
24:01–35:09
7
How does the Nemotron family (Nano, Super, Ultra) differ and what are their use cases?
35:09–1:02:44
8
What is the current state of AI safety and how do open vs closed models compare?
1:02:44–1:22:57
Speakers
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