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GOVERNING DOWNLOAD TERMS: Use of this container is governed by the NVIDIA Software License and Product-Specific Terms for NVIDIA AI Products, and use of the model(s) is governed by OpenMDW-1.1.

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Cosmos3 Container Overview

Description:

This container houses Cosmos3 model family. It is a suite of omnimodal world models designed to jointly process and generate language, images, video, audio, and action sequences within a unified Mixture-of-Transformers architecture. By supporting highly flexible input-output configurations, it seamlessly unifies critical modalities for Physical AI — effectively subsuming vision-language models, video generators, world simulators, and world-action models into a single framework.

The container components are ready for commercial or non-commercial use.

License/Terms of Use:

GOVERNING TERMS: Use of this NIM is governed by the NVIDIA Software License Agreement and the Product-Specific Terms for NVIDIA AI Products and Use of the model(s) is governed by the Linux Foundation’s OpenMDW 1.1 license

You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.

Deployment Geography:

Global

Release Date:

Hugging Face 05/31/2026 via https://huggingface.co/collections/nvidia/cosmos3
\nGitHub 05/31/2026 via https://github.com/nvidia/cosmos

Program Classes:

The Cosmos3 Unified Container includes the following models:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Model Name & Link	Use Case	How to Pull the Model
Cosmos3-Nano	For multimodal understanding, world simulation, future prediction, action reasoning, and Physical AI applications.	[Automatic]
Cosmos3-Super	For multimodal understanding, world simulation, future prediction, action reasoning, and Physical AI applications.]	[Automatic]
Cosmos3-Super-Text2Image	Given text input, generate high-fidelity images that are consistent with the provided description]	[Automatic]
Cosmos3-Super-Image2Video	Given one input image and text instructions, generate temporally coherent video sequences that are consistent with the provided visual content]	[Automatic]
Cosmos3-Super-Image2Video-4Step	Given one or more input images and optional text instructions, generate temporally coherent video sequences	[Automatic]
Cosmos3-Super-Text2Image-4Step	Given text input, generate high-fidelity images that are consistent with the provided description.	[Automatic]
Cosmos3-Nano-Policy-DROID	Given language instructions and visual observations from the DROID robot platform, generate robot action trajectories	[Automatic]

Deployment Details:

Visit the Cosmos cookbook page for release documentation, deployment guides and more.
\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Container Version(s):

nvcr.io/nim/nvidia/cosmos3::latest
nvcr.io/nim/nvidia/cosmos3:2.0.0

Security Common Vulnerabilities and Exposures (CVEs)

Please review the Security Scanning tab on NGC to view the latest security scan results.\nFor certain open-source vulnerabilities listed in the scan results, NVIDIA provides a response in the form of a Vulnerability Exploitability eXchange (VEX) document. The VEX information can be reviewed and downloaded from the Security Scanning tab.

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal developer team to ensure these software components meet requirements for the relevant industry and use case and address unforeseen product misuse.

Users are responsible for model inputs and outputs. Users are responsible for ensuring safe integration of this model, including implementing guardrails as well as other safety mechanisms, prior to deployment.

Please report security vulnerabilities or NVIDIA AI Concerns here.

Get Help

Getting started with the NIM

Deploying and integrating the NIM is straightforward thanks to our industry standard APIs. Visit the Cosmos3 NIM cookbook for release documentation, deployment guides and more.

Enterprise Support

Get access to knowledge base articles and support cases or submit a ticket.

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\n\nThe container components are ready for commercial or non-commercial use.
\n\n\n### License/Terms of Use:\nGOVERNING TERMS: Use of this NIM is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and the [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/) and Use of the model(s) is governed by the Linux Foundation’s [OpenMDW 1.1 license](https://openmdw.ai/license/1-1/)\n\n\n**You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.**\n\n### Deployment Geography:
\nGlobal
\n\n### Release Date:
\nHugging Face 05/31/2026 via [https://huggingface.co/collections/nvidia/cosmos3](https://huggingface.co/collections/nvidia/cosmos3)
\nGitHub 05/31/2026 via [https://github.com/nvidia/cosmos](https://github.com/nvidia/cosmos)\n\n## Program Classes:
\n\nThe Cosmos3 Unified Container includes the following models:
\n\n| Model Name & Link | Use Case | How to Pull the Model |\n| :--- | :--- | :--- |\n| [Cosmos3-Nano](https://huggingface.co/nvidia/Cosmos3-Nano) | For multimodal understanding, world simulation, future prediction, action reasoning, and Physical AI applications. | [Automatic] |\n| [Cosmos3-Super](https://huggingface.co/nvidia/Cosmos3-Super) | For multimodal understanding, world simulation, future prediction, action reasoning, and Physical AI applications.] | [Automatic] |\n| [Cosmos3-Super-Text2Image](https://huggingface.co/nvidia/Cosmos3-Super-Text2Image) | Given text input, generate high-fidelity images that are consistent with the provided description] | [Automatic] |\n| [Cosmos3-Super-Image2Video](https://huggingface.co/nvidia/Cosmos3-Super-Image2Video) | Given one input image and text instructions, generate temporally coherent video sequences that are consistent with the provided visual content] | [Automatic] |\n| [Cosmos3-Super-Image2Video-4Step](https://huggingface.co/nvidia/Cosmos3-Super-Image2Video-4Step)| Given one or more input images and optional text instructions, generate temporally coherent video sequences | [Automatic] |\n| [Cosmos3-Super-Text2Image-4Step](https://huggingface.co/nvidia/Cosmos3-Super-Text2Image-4Step)| Given text input, generate high-fidelity images that are consistent with the provided description. | [Automatic] |\n| [Cosmos3-Nano-Policy-DROID](https://huggingface.co/nvidia/Cosmos3-Nano-Policy-DROID)| Given language instructions and visual observations from the DROID robot platform, generate robot action trajectories | [Automatic] |\n\n\n\n\n## Deployment Details:
\nVisit the Cosmos cookbook [page](https://github.com/ekrivovnv/cosmos/tree/egor/cosmos3_nim_docs_final/cookbooks/cosmos3/nim) for release documentation, deployment guides and more.
\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
\n\n\n## Container Version(s): \n- nvcr.io/nim/nvidia/cosmos3::latest\n- nvcr.io/nim/nvidia/cosmos3:2.0.0\n\n\n## Security Common Vulnerabilities and Exposures (CVEs)\nPlease review the Security Scanning tab on NGC to view the latest security scan results.\nFor certain open-source vulnerabilities listed in the scan results, NVIDIA provides a response in the form of a Vulnerability Exploitability eXchange (VEX) document. The VEX information can be reviewed and downloaded from the Security Scanning tab.\n\n## Ethical Considerations:\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal developer team to ensure these software components meet requirements for the relevant industry and use case and address unforeseen product misuse. \n\nUsers are responsible for model inputs and outputs. Users are responsible for ensuring safe integration of this model, including implementing guardrails as well as other safety mechanisms, prior to deployment.\n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n# Get Help\n\n## Getting started with the NIM\nDeploying and integrating the NIM is straightforward thanks to our industry standard APIs. Visit the [Cosmos3 NIM cookbook](https://github.com/ekrivovnv/cosmos/tree/egor/cosmos3_nim_docs_final/cookbooks/cosmos3/nim) for release documentation, deployment guides and more.\n\n## Enterprise Support\nGet access to knowledge base articles and support cases or [submit a ticket](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).","displayName","Cosmos3 NIM",["D",1788195503430],"publisher","NVIDIA","latestVersion","versions",[144,202],"imageUrl","/api/image/nim/nvidia/containers/cosmos3","rawImageUrl","canGuestDownload","latestVersionSizeInBytes",8876209972,"licenseTerms",{"_268":269,"_270":16,"_271":272,"_273":20,"_274":275},"isMultinodeEnabled","isMultiArchitecture","hasSignedTag","featureLabels",[259,260],"displayLabels",[254],"policyLabels",[],"systemLabels",[247],"products",[],"hasNimMetadata",{"_217":248,"_249":248,"_250":251,"_76":252},"signed images","value","filter",{"_253":252,"_249":248},"system","field",{"_217":255,"_249":256,"_250":257,"_76":82},"NVIDIA AI","soln_nvidia_ai",{"_253":258,"_249":256},"technology",{"_217":266,"_249":266,"_250":267,"_76":264},{"_217":261,"_249":262,"_250":263,"_76":264},"NVIDIA NIM","nimmcro_nvidia_nim",{"_253":265,"_249":262},"feature","nvidia_nim","NVIDIA AI Enterprise Supported",{"_253":82,"_249":266},"governingTerms","

GOVERNING DOWNLOAD TERMS: Use of this container is governed by the NVIDIA Software License and Product-Specific Terms for NVIDIA AI Products, and use of the model(s) is governed by OpenMDW-1.1.

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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616],[563,564,565,566,567],{"_401":477,"_217":477,"_173":403,"_374":587},{"_401":451,"_217":451,"_173":403,"_374":582},{"_401":577,"_217":577,"_173":403,"_374":578},{"_401":572,"_217":572,"_173":403,"_374":573},{"_401":568,"_217":568,"_173":403,"_374":569},"nvidia.partners",[570],{"_203":571,"_408":568,"_409":20,"_173":445},"edge","app.hushly.com",[574],{"_203":575,"_408":572,"_409":20,"_173":576},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[579],{"_203":580,"_408":577,"_409":20,"_173":581},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703],[655,656,657,658],{"_401":670,"_217":670,"_173":403,"_374":671},{"_401":665,"_217":665,"_173":403,"_374":666},{"_401":464,"_217":464,"_173":403,"_374":662},{"_401":577,"_217":577,"_173":403,"_374":659},[660],{"_203":661,"_408":577,"_409":18,"_173":581},"_gh_sess",[663],{"_203":664,"_408":464,"_409":18,"_173":471},"YSC","bizible.com",[667],{"_203":668,"_408":665,"_409":20,"_173":669},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[672],{"_203":673,"_408":670,"_409":20,"_173":674},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_203":745,"_408":523,"_409":20,"_173":746},{"_203":730,"_408":744,"_409":18,"_173":731},{"_203":713,"_408":526,"_409":20,"_173":638},{"_203":713,"_408":626,"_409":20,"_173":638},{"_203":742,"_408":523,"_409":20,"_173":743},{"_203":730,"_408":529,"_409":18,"_173":731},{"_203":740,"_408":523,"_409":20,"_173":741},{"_203":730,"_408":526,"_409":18,"_173":731},{"_203":738,"_408":625,"_409":20,"_173":739},{"_203":730,"_408":647,"_409":18,"_173":731},{"_203":710,"_408":526,"_409":20,"_173":712},{"_203":736,"_408":523,"_409":18,"_173":737},{"_203":734,"_408":523,"_409":20,"_173":735},{"_203":732,"_408":523,"_409":18,"_173":733},{"_203":730,"_408":621,"_409":18,"_173":731},{"_203":728,"_408":523,"_409":20,"_173":729},{"_203":726,"_408":523,"_409":20,"_173":727},{"_203":724,"_408":523,"_409":18,"_173":725},{"_203":722,"_408":630,"_409":20,"_173":723},{"_203":710,"_408":621,"_409":20,"_173":712},{"_203":720,"_408":523,"_409":20,"_173":721},{"_203":718,"_408":523,"_409":18,"_173":719},{"_203":716,"_408":523,"_409":20,"_173":717},{"_203":714,"_408":523,"_409":20,"_173":715},{"_203":713,"_408":523,"_409":20,"_173":638},{"_203":710,"_408":711,"_409":20,"_173":712},{"_203":708,"_408":523,"_409":20,"_173":709},{"_203":706,"_408":523,"_409":18,"_173":707},{"_203":704,"_408":523,"_409":20,"_173":705},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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