NVIDIA Announces Major Release of Cosmos World Foundation Models and Physical AI Data Tools

• New Models Enable Prediction, Controllable World Generation and Reasoning for Physical AI
• Two New Blueprints Deliver Massive Physical AI Synthetic Data Generation for Robot and Autonomous Vehicle Post-Training
• 1X, Agility Robotics, Figure AI, Skild AI Among Early Adopters
  
  

SAN JOSE, Calif., March 18, 2025 (GLOBE NEWSWIRE) -- GTC—NVIDIA today announced a major release of new world foundation models (WFMs), introducing an open and fully customizable reasoning model for development and giving developers unprecedented control over world generation.

NVIDIA is also launching two new blueprints — powered by the and Cosmos platforms — that provide developers with massive, controllable synthetic data generation engines for post-training robots and autonomous vehicles.

Industry leaders including 1X, , Figure AI, Foretellix, Skild AI and Uber are among the first to adopt Cosmos to generate richer training data for physical AI faster and at scale.

“Just as large language models revolutionized generative and agentic AI, Cosmos world foundation models are a breakthrough for physical AI,” said Jensen Huang, founder and CEO of NVIDIA. “Cosmos introduces an open and fully customizable reasoning model for physical AI and unlocks opportunities for step-function advances in robotics and the physical industries.”

Cosmos Transfer for Synthetic Data Generation

Cosmos Transfer WFMs ingest structured video inputs such as segmentation maps, depth maps, lidar scans, pose estimation maps and trajectory maps to generate controllable photoreal video outputs.

Cosmos Transfer streamlines perception AI training, transforming 3D simulations or ground truth created in Omniverse into photorealistic videos for large-scale, controllable .

Agility Robotics will be an early adopter of Cosmos Transfer and Omniverse for large-scale synthetic data generation to train its .

“Cosmos offers us an opportunity to scale our photorealistic training data beyond what we can feasibly collect in the real world,” said Pras Velagapudi, chief technology officer of Agility Robotics. “We’re excited to see what new performance we can unlock with the platform, while making the most use of the physics-based simulation data we already have.”

The uses Cosmos Transfer to amplify variations of physically based sensor data. With the blueprint, Foretellix can enhance behavioral scenarios by varying conditions like weather and lighting for diverse driving datasets. Parallel Domain is also using the blueprint to apply similar variation to its sensor simulation.

The combines Omniverse and Cosmos Transfer to generate diverse datasets at scale, benefiting from -powered simulations and reducing data collection and augmentation time from days to hours.

Cosmos Predict for Intelligent World Generation

at the CES trade show in January, Cosmos Predict WFMs generate virtual world states from multimodal inputs like text, images and video. New will enable multi-frame generation, predicting intermediate actions or motion trajectories when given start and end input images. Purpose-built for post-training, these models can be customized using NVIDIA’s openly available .

With the inference compute power of systems and their large domain, developers can achieve real-time world generation.

1X is using Cosmos Predict and Cosmos Transfer to train its new humanoid robot NEO Gamma. Robot brain developer Skild AI is tapping into Cosmos Transfer to augment synthetic datasets for its robots. Plus, Nexar and Oxa are using Cosmos Predict to advance their autonomous driving systems.

Multimodal Reasoning for Physical AI

Cosmos Reason is an open, fully customizable WFM with spatiotemporal awareness that uses chain-of-thought reasoning to understand video data and predict the outcomes of interactions — such as a person stepping into a crosswalk or a box falling from a shelf — in natural language.

Developers can use Cosmos Reason to improve physical AI data annotation and curation, enhance existing world foundation models or create new vision language action models. They can also post-train it to build high-level planners to tell the physical AI what it needs to do to complete a task.

Accelerating Data Curation and Post-Training for Physical AI

Based on their downstream task, developers can post-train Cosmos WFMs using native PyTorch scripts or the ™ framework on .

Cosmos developers can also use on DGX Cloud for accelerated data processing and curation. Linker Vision and are using it for curating large amounts of video data to train large vision language models for visual agents built on the . is exploring it to be deployed in future surgical robots, while Uber and Waabi are advancing autonomous vehicles development.

Driving Responsible AI and Content Transparency

In line with NVIDIA’s trustworthy AI principles, NVIDIA enforces open across all Cosmos WFMs. In addition, NVIDIA is collaborating with Google DeepMind to integrate to watermark and help identify AI-generated outputs from the Cosmos WFM NVIDIA NIM™ microservice featured on .

Availability

are available for preview in the and now listed in the Vertex AI on Google Cloud. Cosmos Predict and Cosmos Transfer are openly available on and . Cosmos Reason is available in early access.

Learn more by watching the and by registering for from NVIDIA and industry leaders at the show, including “” with Ming-Yu Liu, vice president of generative AI research at NVIDIA.

About NVIDIA

(NASDAQ: NVDA) is the world leader in accelerated computing.

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Paris Fox

Corporate Communications

NVIDIA Corporation

Certain statements in this press release including, but not limited to, statements as to: the benefits, impact, availability, and performance of NVIDIA’s products, services, and technologies; third parties adopting NVIDIA’s products and technologies and the benefits and impact thereof; and Cosmos opening opportunities for step-function advances in robotics and the physical industries are forward-looking statements that are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners' products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems; as well as other factors detailed from time to time in the most recent reports NVIDIA files with the Securities and Exchange Commission, or SEC, including, but not limited to, its annual report on Form 10-K and quarterly reports on Form 10-Q. Copies of reports filed with the SEC are posted on the company's website and are available from NVIDIA without charge. These forward-looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, NVIDIA disclaims any obligation to update these forward-looking statements to reflect future events or circumstances.

© 2025 NVIDIA Corporation. All rights reserved. NVIDIA, the NVIDIA logo, NVIDIA Cosmos, NVIDIA DGX, NVIDIA NeMo, NVIDIA NIM, NVIDIA Omniverse and NVLink are trademarks and/or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Other company and product names may be trademarks of the respective companies with which they are associated. Features, pricing, availability and specifications are subject to change without notice.

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