Why NVIDIA Is Giving Away AI Models | Bryan Catanzaro

NVIDIA is a chip company. So why does it put hundreds of researchers on building AI models — and then give them away for free? Bryan Catanzaro is VP of Applied Deep Learning Research at NVIDIA and one of the people whose work quietly underpins modern AI: he helped create cuDNN (NVIDIA's first deep learning product), co-invented DLSS, and named and built Megatron, the framework behind how much of the industry trains large models. Today he leads Nemotron, NVIDIA's family of open models — and Nemotron 3 Ultra, released just weeks ago, is one of the strongest open-weights models to come out of the US. Matt Turck sits down with Bryan for a genuinely deep conversation: the real business logic behind a chip company building its own models, the state of open vs. closed AI, and whether the US is falling behind China in open models. Then they go inside Nemotron itself — four-bit (NVFP4) pretraining, hybrid Mamba-Transformer architecture, mixture-of-experts, multi-token prediction, and multi-teacher distillation — all explained in plain language. Plus a rare look at how a modern AI research org actually runs, what it was like working alongside Andrew Ng and Dario Amodei at Baidu, why Bryan doesn't believe in the singularity, and his contrarian case that open AI is safer than closed. A reference conversation for anyone trying to understand where AI is really headed. Bryan Catanzaro LinkedIn - https://www.linkedin.com/in/bryancatanzaro X/Twitter - https://x.com/ctnzr NVIDIA Website - https://www.nvidia.com X/Twitter - https://x.com/nvidia Matt Turck (Managing Director) Blog - https://mattturck.com LinkedIn - https://www.linkedin.com/in/turck/ X/Twitter - https://x.com/mattturck FirstMark Website - https://firstmark.com X/Twitter - https://x.com/FirstMarkCap Listen on: Spotify - https://open.spotify.com/show/7yLATDSaFvgJG80ACcRJtq Apple - https://podcasts.apple.com/us/podcast/the-mad-podcast-with-matt-turck/id168623872 00:00 — Cold open & Intro 01:33 — Is open source AI catching the frontier? 05:29 — Do closed labs blocking distillation slow open source down? 07:42 — Is the US falling behind China? 10:30 — Why companies actually choose open models 12:39 — A "crazy" 2008 bet: machine learning on GPUs 15:33 — Working with Andrew Ng and Dario Amodei at Baidu 17:41 — Coming back to NVIDIA: DLSS and the birth of Megatron 21:55 — The real reason NVIDIA builds its own models 24:28 — Is Moore's Law really dead? 33:37 — The Nemotron family: Nano, Super, Ultra 35:09 — Built for agents: why NVIDIA bets on speed 36:02 — How you train a 550B model in 4 bits 39:25 — Hybrid Mamba-Transformer, explained simply 42:31 — Mixture of experts — and why NVIDIA built NVL72 around it 47:26 — Why a 1-million-token context window matters 49:26 — Multi-token prediction: how the model predicts 5 tokens at once 52:47 — Multi-teacher distillation: teaching one model from many 58:01 — Where reinforcement learning goes next 01:00:16 — Inside NVIDIA's research org: "the mission is the boss" 01:04:03 — How NVIDIA decides who gets the GPUs 01:10:53 — Why NVIDIA still feels entrepreneurial after 33 years 01:12:58 — Why Bryan doesn't believe in the singularity 01:17:50 — The AI backlash 01:19:18 — The controversial case: open AI is safer than closed

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