Open models and the future of Physical AI with NVIDIA

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Welcome to another episode of the Practical AI Podcast. This is Daniel Waidnack. I am CEO at Prediction Guard, and I'm joined as always by my co-host, Chris Benson, who is a principal AI and autonomy research engineer. How you doing, Chris?

Hey, I'm doing great today, Daniel. Uh, really excited about today's conversation.

Yeah, well, I think in the world that both of us inhabit, we are open to interesting discussions. And I think we'll talk about things open and things related to the world in today's episode because we have with us Mingyu Liu, who is Vice President of Cosmos Lab at NVIDIA. Welcome to the show, Mingyu.

Hello. And to have me here, great to be here. Hi, Daniel and Chris. Yeah, yeah.

Great to have you. And one of the things I kind of alluded to is maybe something open, which I know is dear to NVIDIA's heart, which is open models. And I've seen in the news recently in what NVIDIA has posted, obviously they are promoting open models as a key piece of what is important. in the AI ecosystem. I'm wondering, Mingyu, if you could help us understand maybe from your perspective, from NVIDIA's perspective, why in today's world is open— is the idea of having open models and promoting open models, why is that such a critical thing in the ecosystem?

Yes. Okay. So when I was a student, I learned a lot about computer vision and machine learning. At that time, there are great researchers, students open source their code and model. And looking at their code and model actually helped me understand the concept and helped me to innovate, right? And this, the academy always worked like that and has been years. even before GPT, and GPT also have open source model in the past. And that had been, you know, push the field forward, allow innovation. And it's just until recently, the model is getting bigger. The amount of resources required to build a model is tremendous amount. And hence, you know, the amount of open model reduced, right? But open model, Generally, it's there for the field, and it's been very useful to push the field forward. And so NVIDIA wants this to continue to happen. I think the world will be in a better place if we have open models to enable innovation. So people, you know, APIs are great, they solve problems, but they don't give you the insights. You know, it doesn't tell you, it doesn't allow you to tear apart. And miss your idea insight. And also the kind of thing you want to do may be suitable, you want to have a local compute and you don't have an internet connection. So API may not always be the solution. And that's why I think an open model is important to enable people. And NVIDIA is an accelerated computing company. So we build This computer, great computer, GPU, networking devices, and now we also build CPU, SoC, right? We build this computer and we want to enable the whole ecosystem, right? And we generally believe that to better build these computers, we need to know more about the applications, right? And in the old day, you have these computer and you have library and you have software applications, right? And in the modern world, you have computer and you have library still, but you now have models. Model is a new kind of libraries people can use and to build great, amazing applications. You know, maybe all kinds of agents to help you buy stuff or help you do code, all on top of models. And we, NVIDIA, like to build models, like to build open models on top of NVIDIA's computer. There are a couple of reasons why we do that. First, we, through building the model, we better understand what kind of computer we should build, what kind of GPU architecture we should have so that NVIDIA can always be there to help the whole ecosystem. And second, we also want to make sure people are enabled to do amazing applications, right? So when we have open model there, And people can test this out first, you know, and combine them to do application good for their targeted business and gain some familiarity. And maybe later they can build their own model. Yeah. So there's a lot of reasons why, you know, open model is critical both for academy and also for the whole industry.

Yeah.

You were kind of getting to a follow-up that I wanted to have, which is it's important to have open models, to your point, to promote innovation. But I'm wondering, in what you're seeing at NVIDIA, is it increasingly important also on the commercial side? So like for NVIDIA's customers, right? What is the implication of having access to open models maybe on the commercial side? Do you have any thoughts on that? So it's great if like I'm in a lab and, you know, I have You know, 5 years to work towards, you know, the next innovation, the next types of models. But if I'm in a commercial company, maybe I'm a customer of NVIDIA, I run NVIDIA hardware. How does the existence of open models help me as, you know, in the commercial world, maybe?

Innovation freedom. Innovation freedom. I think that is maybe the 2 words to To summarize, right? So, you know, you can leverage API to solve the problem. And, but with the open model, you can do more than what's available in API. Yeah. So, and I think it'd be the field is finding more and more important application of AI, right? You know, from earlier day of chatbots, And now the coding agent, and we also see more and more agents, they can do things that we didn't think it's possible. People are working very hard to try to figure out how AI can make our life collectively better, right? And there will be application that was not foreseen by the foundation model of the frontier companies who are building AI, right? And then require some customization. Open model allows them, gives them options, gives them a choice to do the innovation they need for their target business. And this way, I think it's good for the whole society.

I think, I know I was kind of following up on your point there. I was very happy recently to see NVIDIA kind of step— there was a moment some months back where Open models in the West were starting to struggle a bit. Some certain unnamed organizations were closing that had been leading the way. And so it was a delight to see NVIDIA step in and take the leadership there and follow through with some other things, the Hugging Face acquisition that's coming in, the work that you're doing. We're kind of going through this turning point toward the next generation of open models and what that looks like, especially with regard to the landscape of physical AI out there, which is my personal passion, as most of our viewers know. And so, I would love if you could kind of As we are pivoting into this new generation of open models in a physical AI context, where it's not all API-based, it's not all frontier models at big services and stuff like that, and we're all getting excited about drones in everyday life, robots in everyday life, and all the different capacities that that can bring benefits, could you kind of lay out the landscape? What is this, as you mesh these things together, What does that look like to you now? What are you seeing as the opportunities in the near term? And kind of how are organizations that work with NVIDIA starting to position to take on this next generation of physical AI that's upon us?

Yeah, thanks for the great question. So I think physical AI is a term that covers several important verticals, like automotive is one. Factory automation is another, and robotics is a third pillar. There could be more, agriculture, construction, many of them. And physical AI are AI deployed in physical device, and physical device perturb the state of the world and complete certain tasks. And the result is shown as material, as the final— Something you can touch, right? That's how I would like to define physical AI. So nowadays, we have some AI in physical devices, but they are preliminary, right? And I think the most exciting form of physical AI, maybe one of the most exciting forms of physical AI, is a humanoid robot. And we envision that humanoid robot will be able to help people accomplish a lot of tasks. And now we don't have humanoid robots surrounding us, but one day we have humanoid robots surrounding us, they will require a computer. They will require a powerful computer to help us process visual input, understand people's instruction, and then self-correction in complicated tasks. And these are great business opportunities for NVIDIA, right? So that's why NVIDIA cares about physical AI. If we can make physical AI come, you know, The dream of physical AI comes true, there will be a lot of opportunity for media. So we are very aligned. So we want to help the field moving forward. Yeah. And yeah, go ahead. Yeah.

And I was wondering, just tying that back to where we started the conversation with open models, do open models have certain implications specifically in the physical AI world because of the constraints or requirements of the physical AI world?

Yes. It's a good question. So I think, for example, one thing, a physical AI model, one popular physical AI model is a policy model. A policy model is a model based on the visual observation and the instruction given by the user or some machine, and it generates some action to complete the task, right? So observation, instruction in, and action out. And Every robot have a different kind of sensor setup, right? Some robot have 2 eyes, some robot have cameras mounted on the gripper. And self-driving car, some car has 11 cameras, some have 7, some have LiDAR sensor. So every physical device, every physical, every robot see the world differently, right? And some of them are unique, right? So it's difficult to have One AI model that can cover all these, right? And when you have open model, the model comes with built-in capability like world understanding or simulation. And from that prior, people can leverage the data they collected from their device and fine-tune. Maybe this initial model only handle one camera. Now you have multi-view. Enabled through post-training and open model. And it's easier, it's more achievable with open model, right? So I think open model gives you the innovation power. So you can customize open model to better tailor for your physical use case. This is important, right? It means better leverage the observation, right? And also give you opportunity to make it more efficient. Yeah. So in the end, the primary evolution will be intelligence per watt, right? And so with this open architecture, you'll be able to do more.

It's amazing to consider this topic of physical AI, how AI agents are being embedded in the physical world around us. But it just emphasizes this point that we need to have operational control of the agents that we're deploying, whether that be in the case of physical AI or in your digital infrastructure within your company where you're deploying agents as a part of your digital workforce. Operational control is critical so you can ensure that those agents are operating based on on the policies that you set, and you can alert when those agents go off script and actually quarantine agents that are behaving in some destructive manner.

That's exactly what we're offering with Prediction Guard, the self-hosted AI control plane.

This is a company that I'm leading personally, and I'm just so thrilled to see that this operational control of AI agents is at the core core of what we're offering. We're seeing companies build and deploy fleets of AI agents without losing control of those agents, and we'd love to show you how we do that. Check us out at predictionguard.com/practicalai to book a time with myself and the team to talk more about how we're enabling this operational control of agents. Again, that's predictionguard.com/practicalai.

So I'd like to take you, kind of expand on what you were talking about going into the break in terms of, you know, you talked about, you know, a couple of things like fine-tuning models and stuff like that, which I think maybe to some people may be a little bit of a lost art because people have become so API dependent on a lot of the closed models 'cause they're used to cloud environments. And as we move into, into physical AI, and you're taking these open models that give so much potential on what you can do with your own creativity and innovation in these physical devices. And you have to add in the sensors, as you talked about, LiDAR and other things, and whatever the robot's effectors are, how it's manipulating the real world. or perturbing the real world, I believe is how you put it earlier. Could you talk, you know, there's inference involved, which now is in a device versus out in the cloud, and there's power, you know, you have to have power to drive these things. So there's all of these considerations that a lot of folks in the AI space haven't had to deal with in the past.

Yeah.

And that gets something out there that you can touch working toward whatever end you're trying to achieve. Could you talk a bit about how you see that and how you put that together? So maybe kind of give a quick primer to folks that are doing AI that are watching or listening, but may not have done physical AI with open models and just kind of lay out all the concerns that they have to start thinking about there. I think that would be really useful, but just as a layout there.

Okay. So basically, it's been the difference in physical AI as compared to the digital AI that people are very familiar with. Okay.

Yeah.

Okay. So let's say you have a robot and trying to complete certain tasks, right? And robot need to react very fast, right? So the processing had to be done real time on spot, right? And also a lot of tasks is batch size 1 inference problem. So API, you can aggregate users, API call from different users and then process them in whole, right? So when you do that, maybe I can go back a little bit, a bit, right? So LLM is a very popular architecture for coding, for chat, right? And LLM is based on the autoregressive transformer. So it's memory bound. And to more efficient, to use the GPU power more efficiently, you can aggregate the API call from different users to fill in the compute, right? But the same architecture, if you put in this embedded device, right, you don't have API call to aggregate, right? So you are not fully utilizing the compute power in that machine. So for some other architecture might be more efficient, right? So, and talking about Because the application is different, right? So it requires different architecture and different thinking so that you can use your power, your watts more efficiently. And also there's a real-time constraint and safety constraint. So because of the setup difference, it's going to promote different architectures.

And maybe that leads naturally into talking about some of the Well, there's all sorts of architectures that NVIDIA has been working on for quite a while. Obviously, you're in the COSMOS lab. I know one of the things that you're working on is world models, COSMOS meaning world, as people might infer. But help those who maybe Imagine someone who has just used LLMs, or maybe that's their only perception of models. Help connect what a world model is and why it might be necessary in this world of physical AI.

Okay. So language model is a model that learns great internal representation through processing human knowledge written in the form of language. Right? And it's optimized for this language representation to generate languages for chat, for code. You know, code is written as a language as well. And world model is from a different perspective. It is from the perspective of, you know, you have a sensory observation of the world and you know those sensors are capturing different angles of the wall, right? And those sensors, those cameras also capture the dynamics, how physics evolve. And people believe that from modeling these signals, you can implicitly encode the physics of the wall. And with the physics kind of something you model, then you can start to do simulation. And if you connect this world representation with language representation, then you can generate explanation that helps you understand why this thing happened. So it's more like one is coming from a language perspective, the other is more like from the observation of the world or physics and try to learn representation. So different way of learning the representations. And of course they have different use cases.

Yeah. And I guess Language is a represent— or it represents part of the knowledge or how people experience the world, but it's only a small piece of how people experience the world, right? Like I'm, I'm looking at you with my eyes. I'm, I'm hearing things. I, as you mentioned, I see things from different perspective. I see how physics is evolving. Is, is, is it true? So, so in an example world model to just make it.

Yeah.

tangible for people. In an LLM, you would basically put in text and get out text, or at least the next predicted token, right? Or in an image generation model, you'd put in text and then get out an image. In the world model case, what is an example sort of input and output? One of those might be, as you mentioned, maybe it's just a representation of the world, and then you build additional things on top of that. But Maybe help people get some intuition about what do you put into a world model and what do you get out when you're actually operating the world model or performing inference or performing prediction with the world model?

Yes. So I want to touch a point before answering this question. So people build a model for a purpose, right? And we live in the world. So we are interested in building some model of the world so we can do a lot of different tasks, right? One is predicting what's going to happen and some is to explain what happened, right? Or sometimes you need to do navigation. So you have a 3D representation of the world. So some people call it world model. To me, world model is a way to solve some tasks, right? And so it's difficult to, to me, difficult to use like the input-output. to kind of define what is a world model. So I would like to say some sort of modeling of the world is a world model. Okay. And from my perspective, because our mission is to build physical— the mission of Kosmos is to build a physical foundation model to help the whole ecosystem. So we realized that in the early days that you need to have world simulation capability. Given the current observation and the action input, what's going to happen in the future? For this world simulation capability, the input is the current observation and the action sequence. Sometimes the action can be described in text, right? And the output is the future, what's going to happen, maybe in video, maybe in audio, and in the future when you have other modality. other modality. So world simulation. And the other thing is world understanding, right? So you want to understand why this— you ask a robot to complete a task and it didn't complete, right? You want to understand what happened, right? So there's an obstacle blocked the pathway of the robot arm, so it couldn't, right? So When the situation like this happen, you found the sensory input, right? You kind of have a modeling what happened, right? But in order to, for people, you know, the receiver and the question asked is a human, right? So you need to connect those world representation to language so it can generate some description that people understand. Yeah. So, and so that is another side of world model understanding. So it's more similar to multimodal LLM. So you have the video and you have the description and you have the text as outputs. And in COSMO, we believe that, you know, one is for world understanding, the other is for world simulation, right? Although they're for different purposes, but they model the same world. So in COSMO-3, our latest COSMO model, we actually fuse them together. Because we believe that the representation can be shared because we live in the same world, right? It's the same stuff. Yeah. So for the world understanding, the input is video, text, output is text. And for world simulation, the input is observation, action, output is text— output is video, sorry. And for me, put together, it becomes an omnimodel, right? You have different inputs.

Yeah.

In Cosmos DB, we actually support text, action, audio, video as the input, and also support text, audio, video, action as outputs. So you can find all combinations, and I think a developer can choose the best way they want to build on top of it.

That was really good. I'd like to ask a clarifying question because there's a couple of terms out there. that I'd like to get your definitions and how they relate a little bit.

Okay.

One is obviously this notion of world model, which you've been diving into, but a number of times, obviously, you've mentioned simulation. And for those who may not be familiar with this, how do you define or relate the notion of simulation, where you could either have more of a simulator like what people may be already familiar with, which also is obviously in gaming and things like that, Um, with the notion of the world model, as you've just explained it, how do those 2 terms relate to each other?

Okay, good question. So, uh, so, um, we have, uh, um, you know, people spending, you know, centuries of years studying how the world works, and they sometimes, uh, put them in equations, right? And, uh, we have, uh, uh, you know, simulated the environment, uh, governed by programs built on top of those equations. You know, how, you know, when 2 things, you know, hit each other, how things are going to happen. Those are what we consider classical simulators, right? So they are based on physics, the rules, you know, people's interpretation of the equations, and then to govern how things move. The scene I described earlier, people generally call it neural simulation. So it's a data-driven way of doing simulation. Instead of directly put the physics in, you saw tons of observation showing different, how 2 things hit together, they kind of separated, or when you pour water to the ground, how water spread out. It showed a lot of different scenes. visual observation to a model, and the model then later can approximate, predict what's going to happen when they see similar patterns. So it's more like a pattern recognition-based way of doing the simulation, right? So one is rule-based, the other is more like data-driven approximation.

And I'm coming from a perspective that I'm not an expert like you or Chris in this area of physical AI and world models. And I'm wondering in my mind, maybe as a non-expert, you're talking about world models, you're talking about simulation, et cetera. If we go all the way back to kind of one of those concrete examples, like the robot or the manufacturing case or the automotive case, where does the world model come into play as you're creating the models that are actually driving decision-making or movement or actions in the robot in the physical world? Where does that fit in versus— Some other things like you mentioned, policy, models, et cetera?

Okay. It's a good question. So in COSMO, we build this foundation model and try to learn a good world representation, right? And this model can be used in many different ways. From the simulation perspective, okay, let's say I'm building a self-driving car policy, right? A policy that can navigate the Name it, to drive the car from destination A to B, right? So as developing the model, I might have many different checkpoints, right? And how do I know that checkpoint A is better than checkpoint B? In the old day, what people do is that they have a fleet of cars and drivers. Whenever I have a new policy and we deploy the policy to the car and the driver kind of Take the car to the field and see, record how good is the policy model. And you can clearly see that this is not scalable, right? It limits how many iterations you can do, right? But with a world model, instead of having your policy deploy the real car, drive in the real world, you can have your policy interact with the world model. Right. When you steer left, what are you going to see? Steer right, what are you going to see? And from this simulation, you can verify the accuracy of a checkpoint. It can help you quickly narrow down a couple of hypotheses you have. And then you have a few ones that you think is truly valuable, and you do the real test. It helps you to get this iteration faster. And now we know in technology, the most important thing is how fast you can do iteration.

Right.

Then you can develop better policy for your self-driving car. It cannot be the same for robots. And this is the world simulation. The representation allows you to do simulation, what it can do for physical AI. The other thing is that this can be also a great backbone for a policy model. In a world model, you have a world representation. It understands, you know, it's able to understand the instruction given by humans, right? And it's when you model the world, right, it generates the pixel space evolution, right? And the pixel space evolution has a strong correlation to the control signal you might need to use to complete certain manipulation tasks. And that correlation become a great regularization to help you build a better policy model when the data is more limited. So I think world representation is the key. And since physical AI is going to operate in the real world, and this world representation encapsulated in this foundation model are going to be useful in many different ways. And it's also kind of important, we need to make it open so that, you know, People can test out and share feedback with us, and then we can then double down on the capability that's more important to help the physical AI ecosystems.

Wow.

It's just amazing to hear Mingyu talk about NVIDIA's effort to make sure that there is a community of open-source enthusiasts, those that are working on open models, to foster a real community of innovation around AI and agents. And that is so practical. That's what we're about here at Practical AI, is that sort of practical value. And we share that with an amazing event that's happening October 15th, just here in a couple weeks, October 15th, 2026, called the Midwest AI Summit. We're one of the media sponsors of this event, and I'm personally going to be an MC at the event. And it's just filled with amazing practical value. There's an AI engineering lounge where you can sit one-on-one with AI experts that actually can practically provide amazing feedback on your roadmap, toolset, architecture, etc. There's gonna be amazing talks from leaders in the field from all over talking about why they've made certain decisions in their, their AI roadmap, how you can deploy agents off of your laptop into cloud environments, so much more, so much practicality. Plus there's free food and other amazing things. This isn't one to miss, so check it out, midwestaisummit.com, and you can use the code PRACTICALAI20 to get 20% off registration. Do not Don't miss this. Register today. Use the code PracticalAI20 to get 20% off registration for the Midwest AI Summit. Find out more at MidwestAISummit.com and use the code PracticalAI20 for 20% off registration.

So I'd like to, uh, I'd like to kind of maybe expand— might be the right word— the conversation into another dimension. And that's the fact that by contrast, in kind of the cloud digital world, people are often used to dealing with an LLM either by itself or it's managing a collection of agents that are doing tasks and stuff. But that interface is very much, there's a human and there's that LLM, and they are agreeing on what's going to happen with the AI agents going and doing things. When you move things into the physical realm, you add the dimension of, you may have all of those things happening, but you also have many physical devices that are beginning to interact together and they're interacting with humans in different ways. And so, the notion of what interactions are gets quite complex. And I was wondering if you could kind of walk us through that a bit and maybe kind of take us into that new dimension of we're not talking about one robot now, or one self-driving car or whatever you want to address, but maybe a whole bunch of them. And maybe you have a city full of them and that's what is mostly there, and they're in all the domains, everything from the ground all the way up and all the way down, maybe undersea and stuff like that. We may have robots eventually in our bodies, nanobots that are doing— so could you kind of lay out what this implies and what some of the extra considerations are that you have to start thinking about to set this new reality up?

Good question. So I've been asking myself what the future is going to look like, right? So I believe Let me take one example, right? So let's say I'm a manufacturer. I build some very cool toy called Robot Dog, okay? And I got an order of, you know, people want a million Robot Dog, right? And I might have a digital agent powered by LLM, help me understand, look at my inventory, help me understand what the materials I have. In different places, right? So there's an agent, an agent to kind of do some high-level planning, right? And now I know that I need to ship certain material from factory B to factory A so that I can manufacture the robot arm in factory A. And then that agent sends a demand to maybe the factory floor agent in factory B. And Factory B will do an easy handshake, right? And that Factory 4 gonna have some robots, some mobile platform, right? And the robot might carry the good to put the mobile platform, and mobile platform put, you know, load in the truck. And the truck go from a self-driving truck, go from Factory B to Factory A, right? And then we start to do the manufacturing. And every station in, you know, in assemble the stuff, they gonna have visual inspection. They're going to have a robot arm, right? There'll be some kind of closed loop and those are going to be operated by another agent, right? So I see the world going to be multi-agent, right? And also go back to my previous viewpoints, right? So in the end, the intelligence per dollar matters, right? And the different tasks, you probably don't need your robot arm to be capable of solving Olympiad math problems, right? You probably Want it to be more sensitive in some kind of manipulation task. So I think we are looking at a world with multi-different specialized agents, and they're going to work together and to complete certain tasks. And Zhong can be one of them, right? So I might be owning a farm, let's say, produce tea, right? And I can have Zhong to collect the tea leaf and then put that in a package. So all of that, I think we like agent because agent is somebody has certain autonomy to behave, to complete tasks on behalf of you, right? It requires that agent have some self-correction capability, right? That's the part we like. And also you kind of put all the problem to agent and it figures out how you want to solve that, right? And so we got to have specialized agent and there's some agent good at distributing tasks and some agent good at complete tasks and they're going to talk together, work together to complete the stuff. Yeah. And that's how I think, that's what I think is going to happen. Yeah. This is my view, by the way.

Yeah. I love that perspective. It really helps bring a concrete visualization to my mind of what this may look like. And it, but it does maybe to some people out there, it also triggers some Well, there's obviously challenges, right, to get there. I almost, I think about AI agents every day in my day-to-day work, not physical AI, but there's this idea of cascading failure of AI agents, right? Which is this sort of idea, oh, like something small happens over here, but all the agents are interacting and there's this sort of system-wide failure. Failure, maybe, obviously if things are interconnected, things can happen, but from your perspective, maybe it's that, maybe it's something else. What do you see as the challenges that, you mentioned we're working towards these innovations, right? You are working towards these, NVIDIA, the industry. What are some of the main challenges in your mind from getting kind of from where we're at now to something that looks more like that? What are some of the categories of those challenges?

Yeah. So I think there are a ton. And the picture I just gave, multiple agents working together to complete production tasks might be decades away, right? So we are not yet there. And verify the correctness is super important, right? So like what you said, so I envision there are going to be also supervisor agents working in the factory floor and trying to capture scenes that are not yet been identified, right? And also building this physical AI also requires strong robots, right? And I know that the humanoid robots, they evolve very fast, but I think that they are not yet kind of ready for major commercial use, right? And also, You know, to achieve full automation, we need our robot be smarter, even the robot arm smarter and more, I would say, more configurable so that you can have a new task and maybe just read the user manual and the robot arm figure out how to assemble this, right? Those are not yet there. So I think there are a lot of problems need to figure out, and I think it's difficult for one company to do it alone. I think we need a whole ecosystem. And that's why I think building this open model is super important, right? Because you enable more people the tool to do the exploration, to do innovation. And I think to achieve that beautiful future, open model is going to play a critical role.

And on that front, I guess, drawing somewhat closer to an end here, NVIDIA is pushing forward this narrative about open models, right? And I'm very personally thankful for that. And you've emphasized that here. How can— maybe there's software engineers, developers, AI practitioners listening to this podcast. How might they even get started in this area, find some of the resources, the open resources that NVIDIA is putting out there? Any suggestions or tips for people that may want to try to start getting into this innovation, you know, in terms of a starting point? And of course, I'll tell our listeners too, we'll include a bunch of show notes as well in our episode where you can click and find some of these things. But any thoughts there, Mingyu?

Yes. So maybe I'll also use this opportunity to define, you know, What open model mean for NVIDIA? So open model is not just have the model open weight allow you to use. We also provide the training framework so that you can take this open model and your own data and post-train to something more tailored for your use case. We even open source data that we created to help you to use those data if you want to build your own open model. And we also have examples, right? NVIDIA is a company that works with all the AI companies, and we also work with all the physical AI companies. And people are very willing to share some of their experience or some success use case, and we often put them in the form of developer blog or example recipe and different places so that people can look at those usable examples and then to start. And nowadays, I see most of people doing research using LLM. Maybe you can use your favorite agent, whether it's Gemini, GPT, or Claude, ask them to look around and find useful materials. And I think in the new world, everybody wants to make sure the material is easily accessible to those agents so they can better convert them to certain form to educate the human developer.

Yeah.

So, yeah, so, you know, pretty much we put Open models in Hugging Face and all the core resources in GitHub. And we have developer docs to talk about how we use this. And in the GitHub, we also have a recipe cookbook to help developers to learn from how others use open models. And what I say is true for Cozmo, it's also true for Nimotran for good and other open models after that.

That's great. And yeah, from just a member of the community, thank you and NVIDIA for the effort that you're putting into it. It's no small effort to maintain all of those things and all of those places. So it's much appreciated.

And this has been a great conversation.

Mingyu, Really appreciate you taking time to walk us through all of these topics and of course the work that you're doing and hope to have you back on the show. Thank you so much for your time.

Thank you. Thank you. Thank you for having me here. And it was a lot of fun.

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