Bryan Catanzaro
VP of Applied Deep Learning Research, NVIDIA. On 2 shows, 2 appearances. The Shows tab opens the full record on each.
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Bryan Catanzaro heads NVIDIA’s Applied Deep Learning Research team, directing projects spanning chip design, graphics, and the Nemotron open-model family. He previously helped create cuDNN, co-invented DLSS, and co-developed the Megatron-LM framework for training large language models.
2shows
2appearances
36statements
13resolved
11supported
0contradicted
85%fully supported
2said about them ↓ Everything Bryan Catanzaro said on any show that made the record, most notable first. Each card names its show and opens the statement there.
Catanzaro: Chinese AI achievements are not driven by a copycat mentality
“I think it's absolutely false to say that you know the achievements of some other country are all being created by sort of, you know, copycat mentality. It's just not true.”
Catanzaro: China has been leading in open community-oriented AI development
“I think there's a chance for the rest of the world to catch up to China in the sense that you know, we can understand the benefits of working together as a community to build technologies for AI in a way that I think China has frankly been leading.”
Catanzaro: The technological singularity is a wrongheaded idea
“The singularity is, although it's an attractive idea, I think that it's a really a wrongheaded idea because it doesn't really take into account these other factors.”
Catanzaro: Open technologies are inherently the safest way to build AI
“I believe that open technologies for AI are inherently the safest way of building AI.”
Catanzaro: Moore's Law has been economically dead for five to ten years
“The original statement of Moore's law was economic, right? It was about, we can afford to put twice as many transistors on the same chip in every, whatever, 24 months, whatever the time period is. And these days that is, Absolutely not the case. It hasn't been…”
Catanzaro: At AI compute limits, intelligence gains require higher efficiency
“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 mo…”
Catanzaro: Multi-token prediction lowers inference costs as model accuracy improves
“With multi-token prediction, the speed that you get is a function of the accuracy of your model. The more accurate your model is, the faster the inference is, the cheaper the inference is, the more accurate it is. That's not usually how it works, but in this c…”
Catanzaro: Humans will primarily interact with AI inside virtual worlds
“And I think that the primary way that people are going to interact with AI is going to be in virtual worlds. Because I think that's going to be the most natural way of interaction in the most useful way.”
Catanzaro: Training data and compute matter more than AI model architecture
“The model is less important than the data. And the compute that goes into training the model. If you have a model that has really excellent compute properties that allows you to scale really well, efficiently to, you know, many thousand of GPUs, the kinds of r…”
Catanzaro: ChatGPT signaled an era where applied AI research dominates academics
“To me, that was a statement that we were entering a new era of AI where applied research starts to dominate, you know, so Chachapiti didn't come out with a fully fledged academic paper that described exactly what they did to make it so awesome. But because the…”
Catanzaro: The greatest risk with AI is failing to adopt it
“I think the scariest thing for me is you know, are we gonna you know, not figure out how to use this technology? Because I think we desperately need it. I think our world desperately needs more intelligence.”
NVIDIA pre-trained Nemotron Ultra and Super natively using 4-bit floating point
“NemoTron Ultra and Super, ah, were pre-trained using four-bit arithmetic. We pre-trained those in MVFP four”
Catanzaro: Nemotron 3's Latent MoE quadruples experts for same inference cost
“Latent MOE is a specific innovation that we have in NemoTron three family. And what it does is actually reduces the amount of communication that has to be sent through NVLink during MOE computations by basically down projecting it. So, you know, every token pr…”
Catanzaro: MoEs have long been the default architecture in frontier AI
“Yeah, I believe MOEs have been the default in Frontier AI for a long time. They're just a really good combination of inference cost and intelligence.”
Catanzaro: About 40% of NVIDIA data center GPUs go to inference
“Jensen said in the earnings call this week that somewhere around 40% of our data center GPUs were going for inference, which I think is you know, pretty amazing and definitely a shift from where things have been a few years ago.”
Catanzaro: NVIDIA Hopper GPUs feature superior circuits designed by internal AI
“Our hopper GPUs, for example, have a lot of circuits in them that were designed by AI that we built ourselves that have better speed and power and cost characteristics than we knew how to build with any other tool.”
Catanzaro: AI community would have found a Transformer alternative without Google
“If Google had not open source that or had not published that paper but if we started seeing like incredible language modeling results we would have figured out some sort of a model that had good scalable properties that that could help with this space.”
Catanzaro: Enterprise data sovereignty is spurring demand for open AI models
“This is really spurring a lot of demand for open technologies for AI.”
Catanzaro: Dario Amodei worked in bioinformatics before deep learning
“At the time he had been working in bioinformatics, so he hadn't been working on deep learning or the things that we call AI these days.”
NVIDIA DLSS Is About 10 Times More Efficient Than Traditional Rendering
“DLSS is our real-time AI for graphics, and it makes a small GPU run like a big GPU. It's about 10 times more efficient because rather than computing the color of every pixel for every frame, we use AI to infer the color.”
NVIDIA DLSS Generates 23 Out of Every 24 Pixels in Games
“These days, 23 out of every 24 pixels is being generated by our AI model when you're using DLSS to play games”
Catanzaro: Any development or deployment of AI benefits NVIDIA's business
“Whenever AI is further developed and further deployed. It's an opportunity for our business. So, so this is you know, we're very explicitly trying to develop our ecosystem because that's good business for us.”
Catanzaro: Combining SSMs and transformers produces smarter AI models than either alone
“Using both of these together was actually better than using either one on their own. And that is independent of the speed benefit. That is just the model is smarter.”
The other half of the tape: Bryan Catanzaro's own voice is left out of every number here. Other people bring the name up 2 times in 2 episodes across the shows. every mention, with the transcript →
Who brings them up most Matt Turck 1Alex Kantrowitz 1
Every mention by year
tap a year for its mentions
Big Technology 1
the MAD Podcast 1
2026 1 mention in 1 episode
2024 1 mention in 1 episode
One line per show, most statements first. The link opens Bryan's full record on that show: the calibration, argument clarity, speaking style and every statement made there.
| Show | Role there | Eps | Statements | Record | |
|---|---|---|---|---|---|
| MADLEDGER | VP of Applied Deep Learning Research, NVIDIA | 1 | 27 | 82% 9/11 | full record on the MAD Podcast → |
| BIG TECHNOLOGYLEDGER | VP of Applied Deep Learning Research, NVIDIA | 1 | 9 | 100% 2/2 | full record on Big Technology → |