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/).","displayName","Cosmos-1.0-Autoregressive-12B",["D",1752691467203],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/cosmos/models/cosmos-1.0-autoregressive-12b","rawImageUrl","https://nvdam.widen.net/content/xbsnft8sm2/jpeg/cosmos-thumbnail-274x152.jpeg?position=c&color=ffffffff&quality=100&u=hdilxr&use=c2ccj","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","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.","featureLabels",[],"displayLabels",[311],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_275":312,"_313":312,"_314":315,"_316":317},"NSPECT-72FQ-Z3GK","value","filter",{"_318":317,"_313":312},"category","label","field",{"_175":1492,"_177":1493},"ngcApps",[1445],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1426":1427,"_1428":1429,"_1430":1431,"_1432":1433,"_1434":330,"_206":354,"_1435":1436,"_1437":57,"_1438":154,"_1439":154,"_1440":1441,"_1442":154,"_1443":1444},"amplitude",{"_1423":1424,"_1026":-7,"_1425":32},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1417":1418,"_1419":1420,"_1421":1422},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1301":236,"_1302":1303,"_1304":1305,"_1306":1307},"announcements",[],"consentState",{"_392":1291,"_1292":154,"_1293":32,"_1294":32,"_1295":1296},"consentConfig",{"_374":375,"_376":377,"_378":379,"_380":381,"_382":383,"_384":385,"_386":387,"_388":389,"_390":391,"_392":393},"showContactAdmin",1783939775692,[],[],[368,369,370,371,372],{"_46":61,"_48":72,"_50":170,"_52":-5,"_54":-5,"_56":57},{"_46":61,"_48":70,"_50":170,"_52":-5,"_54":-5,"_56":57},{"_46":61,"_48":62,"_50":170,"_52":-5,"_54":-5,"_56":57},{"_46":58,"_48":59,"_50":51,"_52":53,"_54":373,"_56":57},{"_46":47,"_48":49,"_50":170,"_52":-5,"_54":-5,"_56":57},3,"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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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. 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Name	Size	Updated	Actions

config.json	555 B	July 16, 2025 UTC	curl wget Download
model.pt	21.53 GB	July 16, 2025 UTC	curl wget Download
README.md	22.42 KB	July 16, 2025 UTC	curl wget Download