The Neuron: AI Explained
NVIDIA’s Kari Briski on How to Use NVIDIA Nemotron Open-Source AI
15 Oct 2025
Learn how to use NVIDIA's Nemotron open-source AI models with VP Kari Briski. We cover what Nemotron is, minimum hardware specs, the difference between Nano/Super/Ultra tiers, when to choose local vs cloud AI, and practical deployment patterns for businesses. Perfect for anyone wanting to run powerful AI locally with full control and privacy.Resources mentioned:NVIDIA Nemotron Models: https://www.nvidia.com/en-us/ai-data-science/foundation-models/nemotron/Start prototyping for free: https://build.nvidia.com/explore/discoverSubscribe to The Neuron newsletter: https://theneuron.aiWatch more AI interviews: https://www.youtube.com/@TheNeuronAI
Chapter 1: What is NVIDIA Nemotron and why is it important?
welcome humans to the neuron podcast i'm corey knowles joined as always by the one and only grant harvey hello hello um today we are unpacking nematron nvidia's open weights model family built for everything from laptops to data centers we'll get concrete what the average person can do what power users can build how businesses should decide between local versus cloud and how to pick between nano super and ultra
And for this, we're joined today by Keri Briske, VP of Generative AI for Enterprise at NVIDIA. Keri, welcome to The Neuron.
Hello. Thank you for having me. Happy to be here.
That's great. We're sure glad to have you and appreciate you taking time out of what I'm sure is a wild schedule to accommodate us.
Anytime. I love it. I love little breaks.
It's fun. So, Carrie, let's start with just like a very simple overview. So what is NVIDIA Nemotron in simple terms and why should engineers care?
NVIDIA Nemotron is a collection of not just foundation models, but it's also all of the data sets that we put out into the open source.
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Chapter 2: What are the differences between the Nano, Super, and Ultra tiers of Nemotron?
It's recipes, and it's also the training framework, algorithms, and research. So it's just all the work that you need in order to build a foundation model. So it's not just the models. It's everything around it. It's not just the model, but it's the ingredients and the recipe, too.
That's awesome.
It is. And that's one of the things that's really impressed me. And I'd like to chat just a little bit about NVIDIA's open weight strategy. You know, you've got more than 500 published models, tons of training data sets and much more. And I'd argue it's, if not among the most, maybe the most transparent approach in the AI space today.
Can you tell me a little bit about that strategy and why it was the right move for NVIDIA?
Yeah, I think, well, it's the right move for us because we are a developer platform. We are an AI developer platform. And the more that you put out the ingredients, it attracts even more developers.
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Chapter 3: When should businesses choose local AI over cloud solutions?
And the more developers that you attract, the more feedback that you get on your product and what you're putting out. And so it was the right move for us because we believe that everyone should be able to do their life's work and make specialized AI. And in order to do that, you need to have all the ingredients.
That's right. I even joked with Grant. I said, even the Groot data and model are out there.
Yeah. If you could make a robot, you have the technology.
Yeah. It's like our three family of models, right? You have Nemotron, you have Cosmos, and you have Groot. Those are kind of our foundation model families.
Yeah, wait, Cosmos, is that the world model one?
World foundation model, that's right. Yeah, that's right.
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Chapter 4: How can developers efficiently use the Nematron suite?
I'm looking in the physical world, yeah.
Nice. That's awesome. So, like, what sets Nemotron apart from, you know, obviously Cosmos and group, but then other language models in its class, like in terms of architecture or training approach or optimizations? Yeah.
Yeah, I think they're very different. So Nemotron is really kind of the brains, the understanding, the reasoning. It started out as a text-based model, but now it also understands images, the nano does, and then also some video coming soon, and we'll get to vision later down the line. But what sets it apart is that, again, the transparency. And also the reason why we build it.
I mean, we also build it for ourselves to develop our next-gen architectures for systems at scale. And so we also are building it for ourselves. So we have to understand how to build it so we understand how the systems work. And then when we put it out into the open, I mentioned how we also released the datasets. When we started releasing the datasets, we had so many...
Chapter 5: What resources are available for using NVIDIA Nemotron?
Even enterprises come out and say, hey, that's fantastic. Can you help me build a model too? Even though we put the ingredients out, we still want to kind of pick your brain and understand how to use those data sets together. So it really sets it apart in that, again, it's trust through transparency.
That's really awesome. And I think such a refreshing approach, to be honest. Yeah.
You said open weights earlier, and I think that it's really open source, right? I think people started to say open weights because people took apart the fact that things weren't open source. Yeah.
You are so right.
I'm so used to. This is an actually open source model. Yeah.
We've run into that so often where we got a cop saying open source.
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Chapter 6: How does transparency in AI models impact developer trust?
This is open weights. But in this case, it's really open source. That's right. That's right. Thank you. I appreciate that.
Yeah.
What can a highly technical person do with the Nematron suite that's really impressive?
Yeah, well, I think the sky's the limit. We use ourselves internally. We have things like deep researchers. You have your own deep researchers if you've ever gone out to Google or Perplexity. And you can imagine that we have data that we do not want to upload into an API internally. And so we have our own deep researchers.
We actually put out that blueprint for others to build their own deep researchers locally and for themselves.
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Chapter 7: What are the minimum hardware requirements for running Nemotron?
you can specialize them. We have a lot of customers who are specializing for their domain, so they're able to take their proprietary data, their IP, their personal data, and be able to specialize it for their use case and their domain, because nobody knows your domain better than you, and you do not want to give away your intelligence, right? So I think that
So that's things you can do with the model. I think what's interesting with some of our recipes is that we put out the recipes and some people have taken our models and distilled them on their own. And when I say distillation, that means taking a larger model and quantizing it or reducing the precision of the weights so it's smaller and faster. So we've seen a lot of people take it and pick up
our algorithms to do neural architecture search to kind of even change the architecture of the model. So if you're a real techie, you can get into the weeds and really do your own thing, like really change the guts of the model with the tools that we've given you.
Okay, so let's talk about a tool that's completely transformed how I personally work. That's WhisperFlow. Imagine being able to write full articles, emails, even take complex notes just by talking. That's what WhisperFlow lets me do. It's hands-free writing that's smart, accurate, and ridiculously fast. For me, it solved a decade-long problem.
Chapter 8: What is the future vision for Nemotron in everyday applications?
I used to cover baseball as a beat reporter and had to file my story deep in the middle of the night. I always wanted a quality dictation tool that would let me get started on the way home. I wanted to be able to just talk my ideas into a doc. I tried several tools. They all came up short. But with WhisperFlow, I can dictate an entire piece, have it cleaned up, and file all while I'm on the go.
No more waiting to get back home, fire up a laptop in the middle of the night to rush something out. I was able to take time that I already had and use it. But it's not just about accessibility. It's about productivity. You'll save hours, get your thoughts down instantly, and stay in flow without breaking a note.
So whether you're a writer, a founder, or just someone who needs to capture ideas quickly and accurately on the go, WhisperFlow makes it effortless. Seriously, you're going to want to check this one out. You'll wonder how you ever worked without it. It's available on Mac, Windows, and iPhone. Visit whisperflow.ai slash neuron today and get started for free.
That's whisperflow.ai slash N-E-U-R-O-N. Tell them the neuron sent you.
And now, obviously, you've told us that Nematron is more than just models. It's the data set, it's research papers, algorithms, libraries, and more. Could you tell us more about everything that is available to people?
Yeah, I think what's interesting about the datasets is that, well, there's two things. We release the datasets that we've either created or acquired as much as we can. Sometimes we have distribution limitations, but then we try and recreate or do some sort of synthetic derivative. that's differential enough to be able to release it.
So what we've learned from the data set that we've acquired or paid for. And so we're actually kind of paying for these data sets and then also giving them back out to the community. The other thing is that the thing about reasoning and reinforcement learning is that you're not necessarily limited by data. You need data, but you can now do synthetic data generation.
And you can create synthetic environments. And so if you think of an environment like a gym, where the more you exercise your model, the better it gets, and you give it different variations. So when it sees a problem in the real world, then it can know, hey, this is similar to something I've done before, and I can go solve this problem.
So if you think of all of the synthetic data, because you're just compute limited at that point. So when we have just GPUs that are doing synthetic data generation, and then we package that up and put that out. And so that's a lot of compute savings for developers in the community, because now we're not only giving away a synthetic data generation, we're also giving out these gym environments.
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