Nvidia Cosmos is redefining how artificial intelligence is integrated into robotics. Announced at the 2025 CES conference, this groundbreaking platform enables developers to train robots using hyperrealistic synthetic data rather than relying solely on real-world data. With its robust capabilities, Cosmos aims to accelerate the evolution of “physical AI,” a term that encompasses the intelligence behind all things robotic.
The core of Nvidia Cosmos lies in its “world foundation models” (WFMs), a family of specialized AI models designed to simulate three-dimensional physical environments. By generating synthetic training data, these models reduce the need for labor-intensive data collection and labeling. Robots tasked with assembling products, interacting with humans, or navigating roads can now be trained more efficiently and cost-effectively.
Training robots in physical AI has historically required millions of hours of real-world footage, which is both expensive and time-consuming. Nvidia’s WFMs eliminate this bottleneck by creating controllable, high-quality synthetic environments tailored to various use cases. From factory floors to city streets, these simulations provide robots with the data needed to adapt and excel in dynamic real-world scenarios.
To complement Cosmos, Nvidia has enhanced its existing Isaac simulation platform, enabling robot builders to generate synthetic data at scale from a handful of examples. Additionally, the company has upgraded its Omniverse platform with a “Mega” operating system, allowing developers to test and optimize robotic systems in digital twin environments before deploying them in reality.
Cosmos models come in three categories: Nano, Super, and Ultra. Each tier caters to different needs, from real-time, low-latency inference to maximum accuracy and quality outputs. The models range from 4 billion to 14 billion parameters, with Ultra models providing the highest fidelity for knowledge transfer.
Beyond WFMs, the Cosmos platform offers specialized tools, including a 12-billion-parameter unsampling model for refining text prompts and a 7-billion-parameter video decoder for augmented reality applications. Guardrail models ensure responsible AI usage, while fine-tuned models generate multisensor views for autonomous vehicles.
The platform’s capabilities stem from extensive training on 9,000 trillion tokens of data, derived from 20 million hours of real-world footage. This includes environments spanning industrial settings, robotics, autonomous driving, and human activities. Developers can also fine-tune models with their own data for tailored applications.
Nvidia Cosmos is positioned as a leader in the burgeoning robotics industry, which is expected to be worth $45 billion by 2029. Its open model license ensures accessibility, allowing developers with Nvidia hardware to freely use its models. The platform is already being adopted by major players, including humanoid robot manufacturers like Galbot, Agility Robotics, and Figure AI, as well as self-driving car companies such as Uber, Wayve, and Waabi.
Nvidia is not alone in the race to simplify AI integration in robotics. Companies like AWS, Unity, and Genesis are also developing tools to push the boundaries of robotics. Genesis, for example, claims its simulation system can train robots 430,000 times faster than real-time. However, Nvidia’s combination of generative AI, synthetic data, and powerful hardware sets Cosmos apart as a trailblazer in physical AI innovation.
Nvidia’s founder and CEO, Jensen Huang, emphasizes the transformative potential of Cosmos and related platforms. “Everything that moves — from cars and trucks to factories and warehouses — will be robotic and embodied by AI,” Huang said. With tools like Cosmos and the Omniverse digital twin operating system, Nvidia is providing the foundational technologies to digitize and automate physical industries.
As Nvidia Cosmos gains traction, it promises to revolutionize robotics development, enabling smarter, more versatile machines. This fusion of generative AI and advanced simulation technology marks a pivotal moment in the journey toward an AI-driven future.