**Physical AI** # NVIDIA Cosmos An open platform for physical AI with world foundation models (WFMs), video data processing libraries, video evaluation, and post-training frameworks. [Download from GitHub](https://github.com/nvidia-cosmos) [Cookbook](https://nvidia-cosmos.github.io/cosmos-cookbook/)   |   [Documentation](https://docs.nvidia.com/cosmos/)   |   [Discord](https://discord.com/invite/nvidiaomniverse) World Foundation Models ## Open Models for World Generation and Understanding [ ](https://images.nvidia.com/aem-dam/Solutions/cosmos/cosmos-predict.mp4) ### Cosmos Predict Leading world generation model, adaptable to any physical AI task or environment. Generate 30s predictive video worlds from text, image, or video with 2B/14B models, or post-train on your data to create custom edge cases, closed-loop policies, and multiview, robot-centric simulations. [Get Started on GitHub](https://github.com/nvidia-cosmos/cosmos-predict2.5) [ ](/content/dam/en-zz/Solutions/ai/cosmos/nvidia-cosmos-transfer-new.mp4) ### Cosmos Transfer Multicontrol model for simulation to photoreal transformation. Pair with physical AI simulation frameworks, such as CARLA or [NVIDIA Isaac Sim™](https://developer.nvidia.com/isaac/sim), to accelerate [synthetic data generation](https://www.nvidia.com/en-us/glossary/synthetic-data-generation.md) across various environments and lighting conditions. [Get Started on GitHub](https://github.com/nvidia-cosmos/cosmos-transfer2.5) [ ](https://images.nvidia.com/aem-dam/Solutions/ai/cosmos/nvidia-cosmos-reason.mp4) ### Cosmos Reason Leading vision language model (VLM) enabling robots and vision AI agents to reason like humans. Combines prior knowledge, physics, and common sense for real-time alerts and actionable insights across public safety, traffic monitoring, logistics, quality inspection, and physical AI. [Get Started on GitHub](https://github.com/nvidia-cosmos/cosmos-reason2) ## Data Processing and Evaluation Speed up efficient dataset processing and evaluation. ### Cosmos Curator Quickly filter, annotate, and deduplicate large amounts of sensor data with Cosmos Curator. [Download Cosmos Curator on GitHub](https://github.com/nvidia-cosmos/cosmos-curate) ### Cosmos Dataset Search Instantly query datasets and retrieve scenarios with NVIDIA Cosmos Dataset Search (CDS). [Try Cosmos Dataset Search](https://build.nvidia.com/nvidia/cosmos-dataset-search) ### Cosmos Evaluator Review and score generative video outputs at scale using Cosmos Evaluator. [Download Cosmos Evaluator on GitHub](https://github.com/nvidia-cosmos/cosmos-evaluator) Use Cases ## How Cosmos Accelerates AI Across Industries Use Cosmos WFMs to simulate, reason, and generate data for downstream pipelines in robotics, autonomous vehicles, and industrial vision systems. 1. Robot Learning 2. Autonomous Vehicle Training 3. Video Analytics AI Agents ### Robot Learning Build custom world models for downstream tasks, environments, camera or sensor layouts, and policies. * Post-train Cosmos Predict for robot-specific views or control policies * Generate synthetic data across environments and lighting conditions with Cosmos Transfer * Post-train Cosmos Reason using the [Cosmos RL](https://github.com/nvidia-cosmos/cosmos-rl) framework to build vision-language-action (VLA) models * Create an end-to-end synthetic data augmentation and evaluation pipeline using the [Physical AI Data Factory Blueprint](https://nvidianews.nvidia.com/news/nvidia-announces-open-physical-ai-data-factory-blueprint-to-accelerate-robotics-vision-ai-agents-and-autonomous-vehicle-development) built on Cosmos [See Examples](https://nvidia-cosmos.github.io/cosmos-cookbook/gallery/robotics_inference.html) ### Autonomous Vehicle Training Generate custom, diverse, and high-fidelity sensor data for safely training, testing, and validating autonomous vehicles. * Amplify existing data diversity with new weather, lighting, and geolocation data using Cosmos Transfer * Expand into multi-sensor views using Cosmos Predict * Create an end-to-end synthetic data augmentation and evaluation pipeline using the [Physical AI Data Factory Blueprint](https://nvidianews.nvidia.com/news/nvidia-announces-open-physical-ai-data-factory-blueprint-to-accelerate-robotics-vision-ai-agents-and-autonomous-vehicle-development) built on Cosmos [See Examples](https://nvidia-cosmos.github.io/cosmos-cookbook/gallery/av_inference.html) ### Video Analytics AI Agents Enhance automation, safety, and operational efficiency across industrial and urban environments. With Cosmos Reason, AI agents can analyze, summarize, and interact with real-time or recorded video streams to: * Deliver real-time question-answering and alerts * Provide rich contextual insights * Extract insights from large-scale video data with NVIDIA Blueprint for [video search and summarization](https://build.nvidia.com/nvidia/video-search-and-summarization) [Learn More](https://www.nvidia.com/en-us/use-cases/video-analytics-ai-agents.md) Starting Options ## Get Started With NVIDIA Cosmos #### 1 Ready to build? Access open models and code directly. [View GitHub](https://github.com/nvidia-cosmos) #### 2 Not ready to build yet? Try Cosmos models in our hosted catalog. [Try Now](https://build.nvidia.com/search?q=cosmos) #### 3 Need help? Start quickly with our hands-on model recipes. [Browse Cookbook](https://nvidia-cosmos.github.io/cosmos-cookbook/) Trustworthy AI ## Supporting the Physical AI Community Cosmos models, guardrails, and tokenizers are available on Hugging Face and GitHub, with resources to tackle data scarcity in training physical AI models. AI Infrastructure ## Get the Best Performance With NVIDIA Blackwell NVIDIA RTX PRO 6000 Blackwell Series Servers accelerate physical AI development for robots, autonomous vehicles, and AI agents across training, synthetic data generation, simulation, and inference. Unlock peak performance for Cosmos world foundation models on NVIDIA Blackwell GB200 for industrial post-training and inference workloads. [Learn More](https://www.nvidia.com/en-us/data-center/rtx-pro-6000-blackwell-server-edition.md) Ecosystem ## Adopted by Leading Physical AI Innovators Model developers from the robotics, autonomous vehicles, and vision AI industries are using Cosmos to accelerate physical AI development. ## Next Steps ### Join the Cosmos Community Connect with Cosmos experts, engage with fellow developers, provide model feedback, and access continued learning through livestreams and recipes. [Join Now](https://discord.com/invite/nvidiaomniverse) #### Cosmos Cookbook A comprehensive guide for working with the NVIDIA Cosmos ecosystem for real-world, domain-specific applications across robotics, simulation, autonomous systems, and physical scene understanding. [Learn More](https://nvidia-cosmos.github.io/cosmos-cookbook/) #### Build Video Analytics AI Agents Use Cosmos Reason with NVIDIA Blueprint for video search and summarization (VSS) to build AI agents for scalable, real-time video understanding. [Try now](https://build.nvidia.com/nvidia/video-search-and-summarization) Resources ## The Latest From Cosmos Developers 1. Latest News 2. Sessions 3. Demos [See All Tech Blogs](https://developer.nvidia.com/blog/tag/cosmos/) [See All Topic News](https://blogs.nvidia.com/blog/tag/cosmos/) [See All](https://www.nvidia.com/en-us/on-demand/playlist/playList-d71fe16e-07d7-4c9c-a34e-e03e3c9a3b4d/) [See All](https://www.youtube.com/playlist?list=PL3jK4xNnlCVc76KbwP4IeLlsFF0F1l7AP) ![YouTube Video](https://img.youtube.com/vi/tGyJO3yR0vU/maxresdefault.jpg) Consent for Optional Cookies (googleCookiePolicyLink)YouTube sets performance, advertising, and other optional cookies(/googleCookiePolicyLink) when you watch embedded videos. To watch this video, you need to turn on optional cookies for the site. 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By clicking “Accept and Play Video,” you will automatically turn on advertising and other optional cookies for the site and accept our (nvidiaTermsOfServiceLink)Terms of Service(/nvidiaTermsOfServiceLink) (which contains important waivers). Please see our (nvidiaPrivacyPolicyLink)Privacy Policy(/nvidiaPrivacyPolicyLink) and (nvidiaCookiePolicyLink)Cookie Policy(/nvidiaCookiePolicyLink) for more information. Cancel Accept and Play Video Alternatively, you can (youtubeLink)watch this video on YouTube(/youtubeLink). ### NVIDIA Cosmos: A World Foundation Model Platform for Physical AI ![YouTube Video](https://img.youtube.com/vi/ZSxYgW-zHiU/maxresdefault.jpg) Consent for Optional Cookies (googleCookiePolicyLink)YouTube sets performance, advertising, and other optional cookies(/googleCookiePolicyLink) when you watch embedded videos. To watch this video, you need to turn on optional cookies for the site. By clicking “Accept and Play Video,” you will automatically turn on advertising and other optional cookies for the site and accept our (nvidiaTermsOfServiceLink)Terms of Service(/nvidiaTermsOfServiceLink) (which contains important waivers). Please see our (nvidiaPrivacyPolicyLink)Privacy Policy(/nvidiaPrivacyPolicyLink) and (nvidiaCookiePolicyLink)Cookie Policy(/nvidiaCookiePolicyLink) for more information. Cancel Accept and Play Video Alternatively, you can (youtubeLink)watch this video on YouTube(/youtubeLink). ### Generating Synthetic Data for Physical AI With NVIDIA Cosmos ![YouTube Video](https://img.youtube.com/vi/zUX-7XgAJN8/maxresdefault.jpg) Consent for Optional Cookies (googleCookiePolicyLink)YouTube sets performance, advertising, and other optional cookies(/googleCookiePolicyLink) when you watch embedded videos. To watch this video, you need to turn on optional cookies for the site. By clicking “Accept and Play Video,” you will automatically turn on advertising and other optional cookies for the site and accept our (nvidiaTermsOfServiceLink)Terms of Service(/nvidiaTermsOfServiceLink) (which contains important waivers). Please see our (nvidiaPrivacyPolicyLink)Privacy Policy(/nvidiaPrivacyPolicyLink) and (nvidiaCookiePolicyLink)Cookie Policy(/nvidiaCookiePolicyLink) for more information. Cancel Accept and Play Video Alternatively, you can (youtubeLink)watch this video on YouTube(/youtubeLink). ### Autonomous Vehicle Simulation With NVIDIA Omniverse and Cosmos ![YouTube Video](https://img.youtube.com/vi/wjCVFfmsai0/maxresdefault.jpg) Consent for Optional Cookies (googleCookiePolicyLink)YouTube sets performance, advertising, and other optional cookies(/googleCookiePolicyLink) when you watch embedded videos. To watch this video, you need to turn on optional cookies for the site. By clicking “Accept and Play Video,” you will automatically turn on advertising and other optional cookies for the site and accept our (nvidiaTermsOfServiceLink)Terms of Service(/nvidiaTermsOfServiceLink) (which contains important waivers). Please see our (nvidiaPrivacyPolicyLink)Privacy Policy(/nvidiaPrivacyPolicyLink) and (nvidiaCookiePolicyLink)Cookie Policy(/nvidiaCookiePolicyLink) for more information. Cancel Accept and Play Video Alternatively, you can (youtubeLink)watch this video on YouTube(/youtubeLink). ### Using NVIDIA Cosmos World Foundation Models for Physical AI Development Previous Next 1. container 1 2. container 2 3. container 3 4. container 3 5. container 3 6. container 3 7. container 3 8. container 3 9. container 3 10. container 3 11. container 3 ## Frequently Asked Questions ### What’s new in NVIDIA Cosmos WFMs? [January 22, 2026] Released research on [Cosmos Policy](https://research.nvidia.com/labs/dir/cosmos-policy/cosmos_policy_index.html) that builds on Cosmos Predict-2 for visuomotor control and planning. [February 9, 2026] Enhanced compute support, quantization and CUDA compatibility for new [Cosmos Reason 2](https://github.com/nvidia-cosmos/cosmos-reason2). [December 19, 2025] Released Cosmos-Predict2.5-2B Diffusers support via [Hugging Face](https://huggingface.co/docs/diffusers/main/en/api/pipelines/cosmos#diffusers.Cosmos2_5_PredictBasePipeline), Cosmos-Predict2.5-2B Text2World [distilled checkpoint](https://huggingface.co/nvidia/Cosmos-Predict2.5-2B/tree/main/base/distilled) on Hugging Face and Distillation [guide](https://github.com/nvidia-cosmos/cosmos-predict2.5/blob/main/docs/distillation.md). [December 19, 2025] Released Image2Image and ImagePrompt capabilities for Cosmos Transfer 2.5. See the inference guide [here](https://github.com/nvidia-cosmos/cosmos-transfer2.5/blob/main/docs/inference_image.md). Explore GitHub for [more](https://github.com/nvidia-cosmos). ### What is the licensing model for Cosmos world foundation models? Cosmos WFMs are available under an [NVIDIA Open Model License](https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf) for all. ### How do I post-train Cosmos models for my downstream applications? Refer to the new [Cosmos Cookbook](https://nvidia-cosmos.github.io/cosmos-cookbook/), which contains step-by-step recipes and post-training scripts to quickly build, customize, and deploy NVIDIA’s Cosmos world foundation models for robotics and autonomous systems. ### Can I build a world model from scratch using tools from the Cosmos platform and my custom or in-house foundation model? Yes, you can leverage Cosmos to build from scratch with your preferred foundation model or model architecture. You can start by using [Cosmos Curator](https://github.com/nvidia-cosmos/cosmos-curate) for video data preprocessing. Then compress and decode your data with [Cosmos tokenizer](https://developer.nvidia.com/blog/state-of-the-art-multimodal-generative-ai-model-development-with-nvidia-nemo/). Once you have processed the data, you can train or fine-tune your model. Using [NVIDIA NIM™ microservices](https://build.nvidia.com/search?q=%22cosmos%22+-nemotron), you can easily integrate your physical AI models into your applications across cloud, data centers, and workstations. You can also use [NVIDIA DGX Cloud](https://www.nvidia.com/en-us/data-center/dgx-cloud/?ncid=pa-srch-goog-542457-DGX-Brand-prsp&_bt=719595959246&_bk=nvidia%20dgx%20cloud&_bm=b&_bn=g&_bg=169380731029&gad_source=1&gclid=EAIaIQobChMI5ofZgLyvigMVlNXCBB0Vsgu8EAAYASAAEgKPbvD_BwE) to train AI models and deploy them anywhere at scale. ### What are the differences between Cosmos Predict, Cosmos Transfer, and Cosmos Reason, and how do they work together? All three are WFMs with distinct roles: * Cosmos Predict generates diverse video scenes from text, image, or video prompts—ideal for post-training on subjects like robots or self-driving cars. * Cosmos Transfer applies multi-control style transfer—changing lighting and environments—on physics-based videos, often created in simulators like NVIDIA Omniverse™. * Cosmos Reason answers queries by reasoning over video and image inputs. Cosmos Reason can generate new and diverse text prompts from one starting video for Cosmos Predict, or critique and annotate synthetic data from Predict and Transfer. ### What is the difference between Cosmos and Omniverse? Omniverse creates realistic 3D simulations of real-world tasks by using different generative APIs, SDKs, and NVIDIA RTX rendering technology. Developers can input Omniverse simulations as instruction videos to Cosmos Transfer models to generate controllable photoreal synthetic data. Together, Omniverse provides the simulation environment before and after training, while Cosmos provides the foundation models to generate video data and train physical AI models. Learn more about [NVIDIA Omniverse](https://www.nvidia.com/en-us/omniverse.md). 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