Cosmos-1.0-Autoregressive: A Suite of Autoregressive-based World Foundation Models

Cosmos | Code | Paper

Model Overview

Description:

Cosmos World Foundation Models: A family of highly performant pre-trained world foundation models purpose-built for generating physics-aware videos and world states for physical AI development.

The Cosmos autoregressive models are a collection of pre-trained world foundation models that are ideal for predicting and rapidly generating video sequences from video or image inputs for physical AI. They can serve as the building block for various applications or research that are related to world generation. The models are ready for commercial use under NVIDIA Open Model license agreement.

Model Developer: NVIDIA

Model Versions

In Cosmos 1.0 release, the Cosmos Autoregressive WFM family includes the following models:

Cosmos-1.0-Autoregressive-4B\n
- Given a 9-frame input video, predicts the future 24 frames.
\n
Cosmos-1.0-Autoregressive-5B-Video2World\n
- Given text description and an image as the first frame, predicts the future 32 frames.
- Given text description and a 9-frame input video, predicts the future 24 frames.
\n
Cosmos-1.0-Autoregressive-12B\n
- Given a 9-frame input video, predicts the future 24 frames.
\n
Cosmos-1.0-Autoregressive-13B-Video2World\n
- Given text description and an image as the first frame, predicts the future 32 frames.
- Given text description and a 9-frame input video, predicts the future 24 frames.
\n

License:

This model is released under the NVIDIA Open Model License. For a custom license, please contact cosmos-license@nvidia.com.

Under the NVIDIA Open Model License, NVIDIA confirms:

Models are commercially usable.
You are free to create and distribute Derivative Models.
NVIDIA does not claim ownership to any outputs generated using the Models or Derivative Models.

Important Note: If you bypass, disable, reduce the efficacy of, or circumvent any technical limitation, safety guardrail or\nassociated safety guardrail hyperparameter, encryption, security, digital rights management, or authentication mechanism contained\nin the Model, your rights under NVIDIA Open Model License Agreement will automatically terminate.

Model Architecture:

Cosmos-1.0-Autoregressive-12B is an autoregressive transformer model designed for world generation. The network is composed of interleaved self-attention and feedforward layers as its building blocks.

Input/Output Specifications

\n
Input
\n
- Input Type(s): Video
- Input Format(s): mp4
- Input Parameters: Three-dimensional (3D)
- Other Properties Related to Input: The input video should be of 1024x640 resolution with at least 9 frames.
\n
\n
Output
\n
- Output Type(s): Video
- Output Format(s): mp4
- Output Parameters: Three-dimensional (3D)
- Other Properties Related to Output: The generated video will be a 24-frame clip with a resolution of 1024x640 pixels, conditioned on the first 9 frames of the input video.
\n

Software Integration:

Runtime Engine(s):

Cosmos

Supported Hardware Microarchitecture Compatibility:

NVIDIA Blackwell
NVIDIA Hopper
NVIDIA Ampere

Note: We have only tested doing inference with BF16 precision.

Operating System(s):

Linux (We have not tested on other operating systems.)

Usage

See Cosmos for details.

Evaluation

Please see our technical paper for detailed evaluations.

Inference Time and GPU Memory Usage

These numbers may vary based on system specifications and are provided for reference only.

\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\">

Offloading Strategy	Cosmos-1.0-Autoregressive-4B	Cosmos-1.0-Autoregressive-12B
No offloading	31.3 GB	47.5 GB
Guardrails	28.9 GB	45.2 GB
Guardrails & Diffusion decoder	28.5 GB	43.1 GB
Guardrails & Diffusion decoder & Tokenizer	27.3 GB	42.9 GB
Guardrails & Diffusion decoder & Tokenizer & AR model	18.7 GB	27.4 GB

End-to-end inference runtime on one H100 without offloading and after model initialization:

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

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

Cosmos-1.0-Autoregressive-4B	Cosmos-1.0-Autoregressive-12B
~62 seconds	~119 seconds

Failure Analysis

Our models now support video extension up to 33 frames. Starting from either a single image or a 9-frame video input, it can generate the remaining frames to reach the 33-frame length (generating 32 or 24 frames respectively).

We have evaluated all eight possible configurations (4 models × 2 vision input types: image or video) using 100 test videos from physical AI domains. Below are the failure rates for each configuration:

\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	Image input	Video input (9 frames)
Cosmos-1.0-Autoregressive-4B	15%	1%
Cosmos-1.0-Autoregressive-5B-Video2World	7%	2%
Cosmos-1.0-Autoregressive-12B	2%	1%
Cosmos-1.0-Autoregressive-13B-Video2World	3%	0%

We define failure cases as videos with severe distortions, such as:

Sudden appearance of large unexpected objects
Video degrading to a single solid color

Note that the following are not considered failures in our analysis:

Static video frames
Minor object distortions or artifacts

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. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

For more detailed information on ethical considerations for this model, please see the subcards of Explainability, Bias, Safety & Security, and Privacy below. Please report security vulnerabilities or NVIDIA AI Concerns here.

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They can serve as the building block for various applications or research that are related to world generation. The models are ready for commercial use under NVIDIA Open Model license agreement.\n\n**Model Developer**: NVIDIA\n\n## Model Versions\n\nIn Cosmos 1.0 release, the Cosmos Autoregressive WFM family includes the following models:\n- [Cosmos-1.0-Autoregressive-4B](https://huggingface.co/nvidia/Cosmos-1.0-Autoregressive-4B)\n - Given a 9-frame input video, predicts the future 24 frames.\n- [Cosmos-1.0-Autoregressive-5B-Video2World](https://huggingface.co/nvidia/Cosmos-1.0-Autoregressive-5B-Video2World)\n - Given text description and an image as the first frame, predicts the future 32 frames.\n - Given text description and a 9-frame input video, predicts the future 24 frames.\n- [Cosmos-1.0-Autoregressive-12B](https://huggingface.co/nvidia/Cosmos-1.0-Autoregressive-12B)\n - Given a 9-frame input video, predicts the future 24 frames.\n- [Cosmos-1.0-Autoregressive-13B-Video2World](https://huggingface.co/nvidia/Cosmos-1.0-Autoregressive-13B-Video2World)\n - Given text description and an image as the first frame, predicts the future 32 frames.\n - Given text description and a 9-frame input video, predicts the future 24 frames.\n\n### License:\nThis model is released under the [NVIDIA Open Model License](https://huggingface.co/nvidia/Cosmos-1.0-Autoregressive-4B/blob/main/LICENSE.pdf). For a custom license, please contact [cosmos-license@nvidia.com](mailto:cosmos-license@nvidia.com).\n\nUnder the NVIDIA Open Model License, NVIDIA confirms:\n\n* Models are commercially usable.\n* You are free to create and distribute Derivative Models.\n* NVIDIA does not claim ownership to any outputs generated using the Models or Derivative Models.\n\n**Important Note**: If you bypass, disable, reduce the efficacy of, or circumvent any technical limitation, safety guardrail or\nassociated safety guardrail hyperparameter, encryption, security, digital rights management, or authentication mechanism contained\nin the Model, your rights under [NVIDIA Open Model License Agreement](https://huggingface.co/nvidia/Cosmos-1.0-Autoregressive-4B/blob/main/LICENSE.pdf) will automatically terminate.\n\n## Model Architecture:\n\nCosmos-1.0-Autoregressive-12B is an autoregressive transformer model designed for world generation. The network is composed of interleaved self-attention and feedforward layers as its building blocks.\n\n## Input/Output Specifications\n\n* **Input**\n\n * **Input Type(s)**: Video\n * **Input Format(s)**: mp4\n * **Input Parameters**: Three-dimensional (3D)\n * **Other Properties Related to Input**: The input video should be of 1024x640 resolution with at least 9 frames.\n\n* **Output**\n * **Output Type(s)**: Video\n * **Output Format(s)**: mp4\n * **Output Parameters**: Three-dimensional (3D)\n * **Other Properties Related to Output**: The generated video will be a 24-frame clip with a resolution of 1024x640 pixels, conditioned on the first 9 frames of the input video.\n\n## Software Integration:\n**Runtime Engine(s):**\n* [Cosmos](https://github.com/NVIDIA/Cosmos)\n\n**Supported Hardware Microarchitecture Compatibility:**\n* NVIDIA Blackwell\n* NVIDIA Hopper\n* NVIDIA Ampere\n\n**Note**: We have only tested doing inference with BF16 precision.\n\n\n\n**Operating System(s):**\n* Linux (We have not tested on other operating systems.)\n\n\n# Usage\n\n* See [Cosmos](https://github.com/NVIDIA/Cosmos) for details.\n\n\n# Evaluation\n\nPlease see our [technical paper](https://research.nvidia.com/publication/2025-01_cosmos-world-foundation-model-platform-physical-ai) for detailed evaluations.\n\n## Inference Time and GPU Memory Usage\n\nThese numbers may vary based on system specifications and are provided for reference only.\n\n| Offloading Strategy | Cosmos-1.0-Autoregressive-4B | Cosmos-1.0-Autoregressive-12B |\n|-------------|---------|---------|\n| No offloading | 31.3 GB | 47.5 GB |\n| Guardrails | 28.9 GB | 45.2 GB |\n| Guardrails & Diffusion decoder | 28.5 GB | 43.1 GB |\n| Guardrails & Diffusion decoder & Tokenizer | 27.3 GB | 42.9 GB |\n| Guardrails & Diffusion decoder & Tokenizer & AR model | 18.7 GB | 27.4 GB |\n\nEnd-to-end inference runtime on one H100 without offloading and after model initialization:\n\n| Cosmos-1.0-Autoregressive-4B | Cosmos-1.0-Autoregressive-12B |\n|---------|---------|\n| ~62 seconds | ~119 seconds |\n\n## Failure Analysis\n\nOur models now support video extension up to 33 frames. Starting from either a single image or a 9-frame video input, it can generate the remaining frames to reach the 33-frame length (generating 32 or 24 frames respectively).\n\nWe have evaluated all eight possible configurations (4 models × 2 vision input types: image or video) using 100 test videos from physical AI domains. Below are the failure rates for each configuration:\n\n| Model | Image input | Video input (9 frames) |\n|:------------------------------------------|:--------------:|:-------------------------:|\n| Cosmos-1.0-Autoregressive-4B | 15% | 1% |\n| Cosmos-1.0-Autoregressive-5B-Video2World | 7% | 2% |\n| Cosmos-1.0-Autoregressive-12B | 2% | 1% |\n| Cosmos-1.0-Autoregressive-13B-Video2World | 3% | 0% |\n\nWe define failure cases as videos with severe distortions, such as:\n\n* Sudden appearance of large unexpected objects\n* Video degrading to a single solid color\n\nNote that the following are not considered failures in our analysis:\n\n* Static video frames\n* Minor object distortions or artifacts\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. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.\n\nFor more detailed information on ethical considerations for this model, please see the subcards of Explainability, Bias, Safety & Security, and Privacy below. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","Cosmos-1.0-Autoregressive-12B",["D",1752691467203],"/api/image/nvidia/cosmos/models/cosmos-1.0-autoregressive-12b","bias","Field | Response\n:---------------------------------------------------------------------------------------------------|:---------------\nParticipation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None\nMeasures taken to mitigate against unwanted bias: | None","explainability","Field | Response\n:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nIntended Application & Domain: | World Generation\nModel Type: | Transformer\nIntended Users: | Physical AI developers\nOutput: | Videos\nDescribe how the model works: | Generates videos based on video inputs\nTechnical Limitations: | The model may not follow the video input accurately.\nVerified to have met prescribed NVIDIA quality standards: | Yes\nPerformance Metrics: | Quantitative and Qualitative Evaluation\nPotential Known Risks: | The model's output can generate all forms of videos, including what may be considered toxic, offensive, or indecent.\nLicensing: | [NVIDIA Open Model License](https://huggingface.co/nvidia/Cosmos-1.0-Autoregressive-4B/blob/main/LICENSE.pdf)","privacy","Field | Response\n:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------\nGeneratable or reverse engineerable personal information? | None Known\nProtected class data used to create this model? | None Known\nWas consent obtained for any personal data used? | None Known\nHow often is dataset reviewed? | Before Release\nIs a mechanism in place to honor data subject right of access or deletion of personal data? | Not Applicable\nIf personal data was collected for the development of the model, was it collected directly by NVIDIA? | Not Applicable\nIf personal data was collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not Applicable\nIf personal data was collected for the development of this AI model, was it minimized to only what was required? | Not Applicable\nIs there provenance for all datasets used in training? | Yes\nDoes data labeling (annotation, metadata) comply with privacy laws? | Yes\nIs data compliant with data subject requests for data correction or removal, if such a request was made? | Not Applicable","safetyAndSecurity","Field | Response\n:---------------------------------------------------|:----------------------------------\nModel Application(s): | World Generation\nDescribe the life critical impact (if present). | None Known\nUse Case Restrictions: | [NVIDIA Open Model License](https://huggingface.co/nvidia/Cosmos-1.0-Autoregressive-4B/blob/main/LICENSE.pdf)\nModel and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. Model checkpoints are made available on Hugging Face, and may become available on cloud providers' model catalog.",[],[285],[],[],[],{"_91":144,"_145":144,"_146":286,"_148":149},{"_150":149,"_145":144},{"_178":1250,"_180":1251},"ngcApps",[1203],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1184":1185,"_1186":1187,"_1188":1189,"_1190":1191,"_1192":298,"_209":324,"_1193":1194,"_1195":51,"_1196":156,"_1197":156,"_1198":1199,"_1200":156,"_1201":1202},"amplitude",{"_1181":1182,"_837":-7,"_1183":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1175":1176,"_1177":1178,"_1179":1180},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787465379884,"1.12.0","defaultSearchSuggestions",{"_1077":234,"_1078":1079,"_1080":1081,"_1082":1083},"announcements",[],"consentState",{"_363":1067,"_1068":156,"_1069":26,"_1070":26,"_1071":1072},"consentConfig",{"_345":346,"_347":348,"_349":350,"_351":352,"_353":354,"_355":356,"_357":358,"_359":360,"_361":362,"_363":364},"showContactAdmin",1787465379885,[],[],[338,339,340,341,342,343,344],{"_40":55,"_42":66,"_44":67,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":64,"_44":67,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":62,"_44":67,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":67,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":56,"_44":67,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":67,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":67,"_46":-5,"_48":-5,"_50":51},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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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.",[594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618],[565,566,567,568,569],{"_404":479,"_91":479,"_90":234,"_377":589},{"_404":453,"_91":453,"_90":234,"_377":584},{"_404":579,"_91":579,"_90":234,"_377":580},{"_404":574,"_91":574,"_90":234,"_377":575},{"_404":570,"_91":570,"_90":234,"_377":571},"nvidia.partners",[572],{"_124":573,"_410":570,"_411":26,"_90":447},"edge","app.hushly.com",[576],{"_124":577,"_410":574,"_411":26,"_90":578},"*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",[581],{"_124":582,"_410":579,"_411":26,"_90":583},"_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.",[677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705],[657,658,659,660],{"_404":672,"_91":672,"_90":234,"_377":673},{"_404":667,"_91":667,"_90":234,"_377":668},{"_404":466,"_91":466,"_90":234,"_377":664},{"_404":579,"_91":579,"_90":234,"_377":661},[662],{"_124":663,"_410":579,"_411":156,"_90":583},"_gh_sess",[665],{"_124":666,"_410":466,"_411":156,"_90":473},"YSC","bizible.com",[669],{"_124":670,"_410":667,"_411":26,"_90":671},"_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",[674],{"_124":675,"_410":672,"_411":26,"_90":676},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_124":747,"_410":525,"_411":26,"_90":748},{"_124":732,"_410":746,"_411":156,"_90":733},{"_124":715,"_410":528,"_411":26,"_90":640},{"_124":715,"_410":628,"_411":26,"_90":640},{"_124":744,"_410":525,"_411":26,"_90":745},{"_124":732,"_410":531,"_411":156,"_90":733},{"_124":742,"_410":525,"_411":26,"_90":743},{"_124":732,"_410":528,"_411":156,"_90":733},{"_124":740,"_410":627,"_411":26,"_90":741},{"_124":732,"_410":649,"_411":156,"_90":733},{"_124":712,"_410":528,"_411":26,"_90":714},{"_124":738,"_410":525,"_411":156,"_90":739},{"_124":736,"_410":525,"_411":26,"_90":737},{"_124":734,"_410":525,"_411":156,"_90":735},{"_124":732,"_410":623,"_411":156,"_90":733},{"_124":730,"_410":525,"_411":26,"_90":731},{"_124":728,"_410":525,"_411":26,"_90":729},{"_124":726,"_410":525,"_411":156,"_90":727},{"_124":724,"_410":632,"_411":26,"_90":725},{"_124":712,"_410":623,"_411":26,"_90":714},{"_124":722,"_410":525,"_411":26,"_90":723},{"_124":720,"_410":525,"_411":156,"_90":721},{"_124":718,"_410":525,"_411":26,"_90":719},{"_124":716,"_410":525,"_411":26,"_90":717},{"_124":715,"_410":525,"_411":26,"_90":640},{"_124":712,"_410":713,"_411":26,"_90":714},{"_124":710,"_410":525,"_411":26,"_90":711},{"_124":708,"_410":525,"_411":156,"_90":709},{"_124":706,"_410":525,"_411":26,"_90":707},"_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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