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.

","resourceVersions",[202,203],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_183":28,"_184":185,"_52":82,"_79":-5,"_83":186,"_85":187,"_188":81,"_92":19,"_88":89,"_189":-5,"_190":-5,"_87":34,"_191":-5,"_192":34,"_193":-5,"_194":34,"_195":-5,"_196":34,"_197":-5,"_198":34,"_199":-5,"_200":34,"_90":201},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/cosmos/models/cosmos-1.0-autoregressive-12b/-","version","-","imageReference","registry","targetPath","cliDownloadCommand","ngc registry model download-version nvidia/cosmos/cosmos-1.0-autoregressive-12b:1.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/cosmos/models/cosmos-1.0-autoregressive-12b/-/version-history","accessCheckResult",{"_182":89},"hasRelatedCollections","showNimMetadataTab","context",{"_178":179,"_180":181},1792891677129,[],[],[171,172,173,174,175,176,177],{"_48":63,"_50":74,"_52":65,"_54":66,"_56":75,"_58":59},{"_48":63,"_50":72,"_52":115,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":65,"_54":66,"_56":71,"_58":59},{"_48":63,"_50":68,"_52":65,"_54":66,"_56":69,"_58":59},{"_48":63,"_50":64,"_52":65,"_54":66,"_56":67,"_58":59},{"_48":60,"_50":61,"_52":53,"_54":55,"_56":62,"_58":59},{"_48":49,"_50":51,"_52":53,"_54":55,"_56":57,"_58":59},"orgName","nvidia","teamName","cosmos","allowed","versionId","description","",["D",1736226315386],["D",1752691486023],"totalSizeInBytes","digest","malwareScanStatus","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance",[],{"_77":152,"_78":205,"_79":-5,"_88":89,"_87":34,"_90":204,"_192":34},{"_77":28,"_78":28,"_79":-5,"_88":89,"_87":34,"_90":204,"_192":34},[],"Latest (1.0)","nvidia/cosmos/cosmos-1.0-autoregressive-12b","key","nvidia/cosmos/models/cosmos-1.0-autoregressive-12b","resourceId","cosmos-1.0-autoregressive-12b","shortDescription","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.","# Cosmos-1.0-Autoregressive: A Suite of Autoregressive-based World Foundation Models\n\n[**Cosmos**](https://huggingface.co/collections/nvidia/cosmos-6751e884dc10e013a0a0d8e6) | [**Code**](https://github.com/NVIDIA/Cosmos) | [**Paper**](https://research.nvidia.com/publication/2025-01_cosmos-world-foundation-model-platform-physical-ai)\n\n\n# Model Overview\n\n## Description:\n**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.\n\nThe 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.\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","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. 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It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.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.","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.",[602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632],[568,569,570,571,572,573],{"_372":454,"_214":454,"_184":185,"_345":597},{"_372":429,"_214":429,"_184":185,"_345":592},{"_372":587,"_214":587,"_184":185,"_345":588},{"_372":582,"_214":582,"_184":185,"_345":583},{"_372":578,"_214":578,"_184":185,"_345":579},{"_372":574,"_214":574,"_184":185,"_345":575},"nvcrm.my.site.com",[576],{"_78":577,"_378":574,"_379":34,"_184":185},"BrowserId","nvidia.partners",[580],{"_78":581,"_378":578,"_379":34,"_184":415},"edge","app.hushly.com",[584],{"_78":585,"_378":582,"_379":34,"_184":586},"*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",[589],{"_78":590,"_378":587,"_379":34,"_184":591},"_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.",3,[703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746],[675,676,677,678,679],{"_372":698,"_214":698,"_184":185,"_345":699},{"_372":693,"_214":693,"_184":185,"_345":694},{"_372":442,"_214":442,"_184":185,"_345":690},{"_372":683,"_214":683,"_184":185,"_345":684},{"_372":587,"_214":587,"_184":185,"_345":680},[681],{"_78":682,"_378":587,"_379":89,"_184":591},"_gh_sess","statcounter.com",[685,686],{"_78":689,"_378":683,"_379":34,"_184":688},{"_78":687,"_378":683,"_379":34,"_184":688},"is_unique","This domain is owned by StatCounter.\nThe main business activity is: Real time visitor analytics","is_visitor_unique",[691],{"_78":692,"_378":442,"_379":89,"_184":448},"YSC","bizible.com",[695],{"_78":696,"_378":693,"_379":34,"_184":697},"_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",[700],{"_78":701,"_378":698,"_379":34,"_184":702},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_78":808,"_378":516,"_379":34,"_184":809},{"_78":806,"_378":516,"_379":89,"_184":807},{"_78":803,"_378":804,"_379":34,"_184":805},{"_78":781,"_378":794,"_379":89,"_184":782},{"_78":801,"_378":516,"_379":34,"_184":802},{"_78":755,"_378":519,"_379":34,"_184":650},{"_78":755,"_378":552,"_379":34,"_184":650},{"_78":799,"_378":516,"_379":34,"_184":800},{"_78":783,"_378":637,"_379":89,"_184":784},{"_78":781,"_378":522,"_379":89,"_184":782},{"_78":797,"_378":516,"_379":34,"_184":798},{"_78":795,"_378":516,"_379":34,"_184":796},{"_78":789,"_378":516,"_379":34,"_184":185},{"_78":780,"_378":794,"_379":34,"_184":423},{"_78":781,"_378":519,"_379":89,"_184":782},{"_78":792,"_378":552,"_379":34,"_184":793},{"_78":790,"_378":642,"_379":34,"_184":791},{"_78":781,"_378":666,"_379":89,"_184":782},{"_78":789,"_378":637,"_379":34,"_184":185},{"_78":753,"_378":519,"_379":34,"_184":754},{"_78":780,"_378":519,"_379":34,"_184":423},{"_78":787,"_378":516,"_379":89,"_184":788},{"_78":785,"_378":516,"_379":34,"_184":786},{"_78":783,"_378":516,"_379":89,"_184":784},{"_78":781,"_378":637,"_379":89,"_184":782},{"_78":780,"_378":637,"_379":34,"_184":423},{"_78":778,"_378":516,"_379":34,"_184":779},{"_78":776,"_378":516,"_379":34,"_184":777},{"_78":774,"_378":516,"_379":89,"_184":775},{"_78":771,"_378":772,"_379":34,"_184":773},{"_78":753,"_378":637,"_379":34,"_184":754},{"_78":769,"_378":516,"_379":89,"_184":770},{"_78":767,"_378":516,"_379":34,"_184":768},{"_78":766,"_378":637,"_379":89,"_184":761},{"_78":764,"_378":516,"_379":89,"_184":765},{"_78":762,"_378":531,"_379":34,"_184":763},{"_78":760,"_378":637,"_379":89,"_184":761},{"_78":758,"_378":516,"_379":34,"_184":759},{"_78":756,"_378":516,"_379":34,"_184":757},{"_78":755,"_378":516,"_379":34,"_184":650},{"_78":753,"_378":531,"_379":34,"_184":754},{"_78":751,"_378":516,"_379":34,"_184":752},{"_78":749,"_378":516,"_379":89,"_184":750},{"_78":747,"_378":516,"_379":34,"_184":748},"_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","Datadog Cookie used to group all events generated from a unique user session across multiple pages. It contains the current session ID, whether the session is excluded due to sampling, and the expiration date of the session. The cookie is extended for an extra 15 minutes every time the user interacts with the website, up to the maximum user session duration (4 hours).","nvweb_A","_ga","This cookie name is associated with Google Universal Analytics - which is a significant update to Google's more commonly used analytics service. This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","s_tp","This cookie is associated with Adobe Analytics. The cookie is used for measuring a visitor’s scroll activity to see how much of a page they view before moving on to another page.","_an_uid","This cookie is likely used for analytics purposes to track user behavior and site usage, which may include unique user identification for tracking.","tp","This cookie is associated with Adobe Analytics site tracking. It registers if the website's scroll-depth detection is active. This function remembers how far the user has scrolled on the current session across the website's sub-pages.","s_ips","_hly_vid","This cookie _hly_vid is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.","s_plt","This cookie is used to measure the time taken for pages to load, allowing website owners to identify slow loading pages and improve user experience.","_session_id","resources.nvidia.com","This cookie creates an interim session ID used as an in-session user ID.\n","s_cc","This cookie is associated with the Adobe Site Catalyst. It determines whether cookies are enabled in the web browser.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. It is used to persist the Hotjar User ID, unique to that site on the browser. This ensures that behavior in subsequent visits to the same site will be attributed to the same user ID.","__sec_*","_hjHasCachedUserAttributes","This cookie name is associated with Hotjar which is used to know whether the data set in _hjUserAttributes Local Storage item is up to date or not.","s_ppv","This cookie is associated with Adobe Analytics site tracking and is used to measure a user's scroll activity to see how much of a page they view before moving to another page.","_gid","This cookie name is associated with Google Universal Analytics. This appears to be a new cookie and as of Spring 2017 no information is available from Google. It appears to store and update a unique value for each page visited.","_hjTLDTest","When the Hotjar script executes we try to determine the most generic cookie path we should use, instead of the page hostname. This is done so that cookies can be shared across subdomains (where applicable). 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users logged in across NVIDIA domains.","api.ngc.nvidia.com","This cookie is managed by AWS and is used for load balancing.","bm_so","This cookie name is used to load the site as quickly as possible and to recognize bots using Akamai.","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.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","omniverse.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.\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.","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","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","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.","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.","nvidiaCart","This domain is owned by NVIDIA Corporation which provides information about the company, its products, solutions, industries, news, events, and career opportunities.","nv_co","__uxq412__ses.88c1","AWS ELB application load balancerUsed 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.","learn.learn.nvidia.com","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","This cookie is associated with Akamai. 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It is a temporary cookie used to determine if the browser accepts cookies.","XSRF-TOKEN","This cookie is written to help with site security in preventing Cross-Site Request Forgery attacks.This cookie helps with site security in preventing Cross-Site Request Forgery attacks.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","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.","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","bm_ss","This cookie is used to help protect websites from automated bot traffic.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. 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