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Field	Response
Participation considerations from adversely impacted groups protected classes in model design and testing:	None
Measures taken to mitigate against unwanted bias:	None

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Cosmos-1.0-Diffusion: A Suite of Diffusion-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 diffusion models are a collection of diffusion based world foundation models that generate dynamic, high quality videos from text, image, or video inputs. It 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 Diffusion WFM family includes the following models:

Cosmos-1.0-Diffusion-7B-Text2World\n
- Given a text description, predict an output video of 121 frames.
\n
Cosmos-1.0-Diffusion-14B-Text2World\n
- Given a text description, predict an output video of 121 frames.
\n
Cosmos-1.0-Diffusion-7B-Video2World\n
- Given a text description and an image as the first frame, predict the future 120 frames.
\n
Cosmos-1.0-Diffusion-14B-Video2World\n
- Given a text description and an image as the first frame, predict the future 120 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-Diffusion-14B-Video2World is a diffusion transformer model designed for video denoising in the latent space. The network is composed of interleaved self-attention, cross-attention and feedforward layers as its building blocks. The cross-attention layers allow the model to condition on input text throughout the denoising process. Before each layers, adaptive layer normalization is applied to embed the time information for denoising. When image or video is provided as input, their latent frames are concatenated with the generated frames along the temporal dimension. Augment noise is added to conditional latent frames to bridge the training and inference gap.

Input/Output Specifications

\n
Input
\n
- Input Type(s): Text+Image, Text+Video
- Input Format(s):\n
  - Text: String
  - Image: jpg, png, jpeg, webp
  - Video: mp4
  \n
- Input Parameters:\n
  - Text: One-dimensional (1D)
  - Image: Two-dimensional (2D)
  - Video: Three-dimensional (3D)
  \n
- Other Properties Related to Input:\n
  - The input string should contain fewer than 300 words and should provide descriptive content for world generation, such as a scene description, key objects or characters, background, and any specific actions or motions to be depicted within the 5-second duration.
  - The input image should be of 1280x704 resolution.
  - The input video should be of 1280x704 resolution and 9 input frames.
  \n
\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 5-second clip with a resolution of 1280x704 pixels at 24 frames per second (fps). The content of the video will use the provided image as the first frame and visualize the input text description as a short animated scene, capturing the main elements mentioned in the input within the time constraints.
\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

The numbers provided below may vary depending on system specs and are 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	7B Video2World	14B Video2World
Offload prompt upsampler	76.5 GB	> 80.0 GB
Offload prompt upsampler & guardrails	59.9 GB	73.3 GB
Offload prompt upsampler & guardrails & T5 encoder	41.3 GB	54.8 GB
Offload prompt upsampler & guardrails & T5 encoder & tokenizer	41.1 GB	54.5 GB
Offload prompt upsampler & guardrails & T5 encoder & tokenizer & diffusion model	27.3 GB	39.0 GB

The following table shows the end-to-end inference runtime on a single H100 GPU, excluding model initialization time:

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

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

7B Video2World (offload prompt upsampler)	14B Video2World (offload prompt upsampler, guardrails)
~383 seconds	~593 seconds

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","resourceVersionsTotal","hasMoreVersions","versionMetadata","versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/cosmos/models/cosmos-1.0-diffusion-14b-video2world/-","version","-","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/cosmos/cosmos-1.0-diffusion-14b-video2world:1.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/cosmos/models/cosmos-1.0-diffusion-14b-video2world/-/version-history","accessCheckResult",{"_104":105,"_106":20},"hasRelatedCollections",true,"context",{"_100":101,"_102":103},1783956029506,[],[],[92,93,94,95,96,97],{"_33":47,"_35":54,"_37":55,"_39":56,"_41":53,"_43":44},{"_33":47,"_35":51,"_37":38,"_39":52,"_41":53,"_43":44},{"_33":47,"_35":50,"_37":38,"_39":52,"_41":99,"_43":44},{"_33":47,"_35":48,"_37":55,"_39":56,"_41":98,"_43":44},{"_33":45,"_35":46,"_37":38,"_39":40,"_41":42,"_43":44},{"_33":34,"_35":36,"_37":38,"_39":40,"_41":42,"_43":44},62,85,"orgName","nvidia","teamName","cosmos","reason","login_required","allowed","id","nvidia/cosmos/cosmos-1.0-diffusion-14b-video2world","key","nvidia/cosmos/models/cosmos-1.0-diffusion-14b-video2world","resourceType","models","resourceId","cosmos-1.0-diffusion-14b-video2world","shortDescription","The Cosmos diffusion models are a collection of diffusion based world foundation models that generate dynamic, high quality videos from text, image, or video inputs.","description","# **Cosmos-1.0-Diffusion**: A Suite of Diffusion-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 diffusion models are a collection of diffusion based world foundation models that generate dynamic, high quality videos from text, image, or video inputs. It 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 Diffusion WFM family includes the following models:\n- [Cosmos-1.0-Diffusion-7B-Text2World](https://huggingface.co/nvidia/Cosmos-1.0-Diffusion-7B-Text2World)\n - Given a text description, predict an output video of 121 frames.\n- [Cosmos-1.0-Diffusion-14B-Text2World](https://huggingface.co/nvidia/Cosmos-1.0-Diffusion-14B-Text2World)\n - Given a text description, predict an output video of 121 frames.\n- [Cosmos-1.0-Diffusion-7B-Video2World](https://huggingface.co/nvidia/Cosmos-1.0-Diffusion-7B-Video2World)\n - Given a text description and an image as the first frame, predict the future 120 frames.\n- [Cosmos-1.0-Diffusion-14B-Video2World](https://huggingface.co/nvidia/Cosmos-1.0-Diffusion-14B-Video2World)\n - Given a text description and an image as the first frame, predict the future 120 frames.\n\n\n### License:\nThis model is released under the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license). 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://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license) will automatically terminate.\n\n## Model Architecture:\n\nCosmos-1.0-Diffusion-14B-Video2World is a diffusion transformer model designed for video denoising in the latent space. The network is composed of interleaved self-attention, cross-attention and feedforward layers as its building blocks. The cross-attention layers allow the model to condition on input text throughout the denoising process. Before each layers, adaptive layer normalization is applied to embed the time information for denoising. When image or video is provided as input, their latent frames are concatenated with the generated frames along the temporal dimension. Augment noise is added to conditional latent frames to bridge the training and inference gap.\n\n## Input/Output Specifications\n\n* **Input**\n\n * **Input Type(s)**: Text+Image, Text+Video\n * **Input Format(s)**:\n * Text: String\n * Image: jpg, png, jpeg, webp\n * Video: mp4\n * **Input Parameters**:\n * Text: One-dimensional (1D)\n * Image: Two-dimensional (2D)\n * Video: Three-dimensional (3D)\n * **Other Properties Related to Input**:\n * The input string should contain fewer than 300 words and should provide descriptive content for world generation, such as a scene description, key objects or characters, background, and any specific actions or motions to be depicted within the 5-second duration.\n * The input image should be of 1280x704 resolution.\n * The input video should be of 1280x704 resolution and 9 input 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 5-second clip with a resolution of 1280x704 pixels at 24 frames per second (fps). The content of the video will use the provided image as the first frame and visualize the input text description as a short animated scene, capturing the main elements mentioned in the input within the time constraints.\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\nThe numbers provided below may vary depending on system specs and are for reference only.\n\n| Offloading Strategy | 7B Video2World | 14B Video2World |\n|----------------------------------------------------------------------------------|---------|---------|\n| Offload prompt upsampler | 76.5 GB | > 80.0 GB |\n| Offload prompt upsampler & guardrails | 59.9 GB | 73.3 GB |\n| Offload prompt upsampler & guardrails & T5 encoder | 41.3 GB | 54.8 GB |\n| Offload prompt upsampler & guardrails & T5 encoder & tokenizer | 41.1 GB | 54.5 GB |\n| Offload prompt upsampler & guardrails & T5 encoder & tokenizer & diffusion model | 27.3 GB | 39.0 GB |\n\nThe following table shows the end-to-end inference runtime on a single H100 GPU, excluding model initialization time:\n\n| 7B Video2World (offload prompt upsampler) | 14B Video2World (offload prompt upsampler, guardrails) |\n|---------|---------|\n| ~383 seconds | ~593 seconds |\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-Diffusion-14B-Video2World","updatedDate",["D",1736232578130],"publisher","NVIDIA","latestVersion","1.0","versions","collectionCounts","imageUrl","/api/image/nvidia/cosmos/models/cosmos-1.0-diffusion-14b-video2world","rawImageUrl","https://nvdam.widen.net/content/xbsnft8sm2/jpeg/cosmos-thumbnail-274x152.jpeg?position=c&color=ffffffff&quality=100&u=hdilxr&use=c2ccj","canGuestDownload","latestVersionSizeInBytes",28534623615,"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://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license)","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://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license)\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",[158],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_119":159,"_160":159,"_161":162,"_163":164},"NSPECT-72FQ-Z3GK","value","filter",{"_165":164,"_160":159},"category","label","field","appConfig",{"_1347":1348,"_200":1349},"ngcApps",[1300],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1281":1282,"_1283":1284,"_1285":1286,"_1287":1288,"_1289":178,"_74":205,"_1290":1291,"_1292":44,"_1293":85,"_1294":85,"_1295":1296,"_1297":85,"_1298":1299},"amplitude",{"_1278":1279,"_880":-7,"_1280":20},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1272":1273,"_1274":1275,"_1276":1277},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","https://api.ngc.nvidia.com","ngcHelmOrigin","https://helm.ngc.nvidia.com","origin","https://catalog.ngc.nvidia.com","user","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1155":286,"_1156":1157,"_1158":1159,"_1160":1161},"announcements",[],"consentState",{"_242":1145,"_1146":20,"_1147":20,"_1148":85,"_1149":1150},"consentConfig",{"_224":225,"_226":227,"_228":229,"_230":231,"_232":233,"_234":235,"_236":237,"_238":239,"_240":241,"_242":243},"showContactAdmin",1783956029369,[],[],[219,220,221,222,223],{"_33":47,"_35":54,"_37":49,"_39":-5,"_41":-5,"_43":44},{"_33":47,"_35":51,"_37":49,"_39":-5,"_41":-5,"_43":44},{"_33":47,"_35":48,"_37":49,"_39":-5,"_41":-5,"_43":44},{"_33":45,"_35":46,"_37":38,"_39":40,"_41":42,"_43":44},{"_33":34,"_35":36,"_37":49,"_39":-5,"_41":-5,"_43":44},"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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We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429],[267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283],{"_284":391,"_119":391,"_117":286,"_257":392},{"_284":386,"_119":386,"_117":286,"_257":387},{"_284":382,"_119":382,"_117":286,"_257":383},{"_284":377,"_119":377,"_117":286,"_257":378},{"_284":372,"_119":372,"_117":286,"_257":373},{"_284":360,"_119":360,"_117":286,"_257":361},{"_284":348,"_119":348,"_117":286,"_257":349},{"_284":343,"_119":343,"_117":286,"_257":344},{"_284":332,"_119":332,"_117":286,"_257":333},{"_284":327,"_119":327,"_117":286,"_257":328},{"_284":322,"_119":322,"_117":286,"_257":323},{"_284":317,"_119":317,"_117":286,"_257":318},{"_284":312,"_119":312,"_117":286,"_257":313},{"_284":307,"_119":307,"_117":286,"_257":308},{"_284":302,"_119":302,"_117":286,"_257":303},{"_284":295,"_119":295,"_117":286,"_257":296},{"_284":285,"_119":285,"_117":286,"_257":287},"hostName","amazon-adsystem.com","",[288,289],{"_251":294,"_291":285,"_292":20,"_117":293},{"_251":290,"_291":285,"_292":20,"_117":293},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[297,298],{"_251":301,"_291":295,"_292":20,"_117":300},{"_251":299,"_291":295,"_292":20,"_117":300},"IDE","This domain is owned by Doubleclick (Google). The main business activity is: Doubleclick is Googles real time bidding advertising exchange","test_cookie","bing.com",[304],{"_251":305,"_291":302,"_292":20,"_117":306},"MUID","This domain is owned by Mircosoft - it is the site for the search engine Bing.","assets.adobedtm.com",[309],{"_251":310,"_291":307,"_292":20,"_117":311},"kndctr_F207D74D549850760A4C98C6_AdobeOrg_consent","If you know anything about this host and its uses please add your contributions here. ","tiktok.com",[314],{"_251":315,"_291":312,"_292":20,"_117":316},"_ttp","TikTok is a social media platform owned by ByteDance, known for short-form video sharing. The service is used to create and share videos, and it utilizes cookies for various purposes including advertising and analytics.","t.co",[319],{"_251":320,"_291":317,"_292":20,"_117":321},"muc_ads","This domain is owned by Twitter. It is used as a URL shortening service, which allows Twitter to collect data on how links are shared.","snowplow.com",[324],{"_251":325,"_291":322,"_292":20,"_117":326},"sp","Testing","google.com",[329],{"_251":330,"_291":327,"_292":20,"_117":331},"NID","This domain is owned by Google Inc. Although Google is primarily known as a search engine, the company provides a diverse range of products and services. Its main source of revenue however is advertising. Google tracks users extensively both through its own products and sites, and the numerous technologies embedded into many millions of websites around the world. It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","tags.srv.stackadapt.com",[334,335,336,337],{"_251":342,"_291":332,"_292":20,"_117":339},{"_251":341,"_291":332,"_292":20,"_117":339},{"_251":340,"_291":332,"_292":20,"_117":339},{"_251":338,"_291":332,"_292":20,"_117":339},"sa-user-id-v3","This domain is associated with StackAdapt, a platform for programmatic advertising, which suggests that it is used for targeting and advertising purposes.","sa-user-id","sa-user-id-v2","sa-user-id-v4","linkedin.com",[345],{"_251":346,"_291":343,"_292":20,"_117":347},"bcookie","This domain is owned by LinkedIn, the business networking platform. It typically acts as a third party host where website owners have placed one of its content sharing buttons in their pages, although its content and services can be embedded in other ways. Although such buttons add functionality to the website they are on, cookies are set regardless of whether or not the visitor has an active Linkedin profile, or agreed to their terms and conditions. For this reason it is classified as a primarily tracking/targeting domain.","twitter.com",[350,351,352,353],{"_251":359,"_291":348,"_292":20,"_117":355},{"_251":357,"_291":348,"_292":20,"_117":358},{"_251":356,"_291":348,"_292":20,"_117":355},{"_251":354,"_291":348,"_292":20,"_117":355},"personalization_id","This domain is owned by Twitter. The main business activity is: Social Networking Services. Where twitter acts as a third party host, it collects data through a range of plug-ins and integrations, that is primarily used for tracking and targeting.","guest_id_marketing","guest_id","Twitter does not currently provide information on the use of specific cookies.","guest_id_ads","youtube.com",[362,363,364,365],{"_251":371,"_291":360,"_292":20,"_117":369},{"_251":370,"_291":360,"_292":20,"_117":369},{"_251":368,"_291":360,"_292":20,"_117":369},{"_251":366,"_291":360,"_292":20,"_117":367},"VISITOR_INFO1_LIVE","This cookie is used as a unique identifier to track viewing of videos","__Secure-YNID","YouTube is a Google owned platform for hosting and sharing videos. YouTube collects user data through videos embedded in websites, which is aggregated with profile data from other Google services in order to display targeted advertising to web visitors across a broad range of their own and other websites.","__Secure-YEC","__Secure-ROLLOUT_TOKEN","demdex.net",[374],{"_251":375,"_291":372,"_292":20,"_117":376},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[379],{"_251":380,"_291":377,"_292":20,"_117":381},"receive-cookie-deprecation","This domain is owned by im-apps.net which provides information and resources about various instant messaging (IM) applications to help users make informed decisions based on their communication needs.","influ2.com",[384],{"_251":385,"_291":382,"_292":20,"_117":326},"R","6sc.co",[388],{"_251":389,"_291":386,"_292":20,"_117":390},"6suuid","This domain is associated with the company 6Sense Insights, Inc., which provides predictive analytics and account engagement services. 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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.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","kndctr_F207D74D549850760A4C98C6_AdobeOrg_identity","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","ai-podcast.nvidia.com","This cookie is used to identify the user's session on the website and is often associated with analytics or personalization.","_gcl_au","Used by Google AdSense for experimenting with advertisement efficiency across websites using their services","_mkto_trk","This cookie is associated with an email marketing service provided by Marketo. This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","_gat_gtag_UA_140825383_2","Google Analytics","_zitok","This cookie name is associated with ZoomInfo which is used to maintain a unique visitor identifier and store Personally Identifiable Information.","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","AMP_91bf70adfe","Supports Amplitude product analytics.","This cookie name is associated with StackAdapt Inc. which is used to support the effective management and optimization of digital advertising campaigns by handling information such as visitor actions on websites, visited pages, time spent, page load speed, and more.","build.nvidia.com","kndctr_F207D74D549850760A4C98C6_AdobeOrg_cluster","_cs_nnnnnnnnnnnnn","This is a session cookie. 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It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","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. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. 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.","AMP_MKTG_91bf70adfe","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.",4,[518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538],[483,484,485,486,487],{"_284":377,"_119":377,"_117":286,"_257":509},{"_284":343,"_119":343,"_117":286,"_257":504},{"_284":497,"_119":497,"_117":286,"_257":498},{"_284":493,"_119":493,"_117":286,"_257":494},{"_284":488,"_119":488,"_117":286,"_257":489},"nvcrm.my.site.com",[490],{"_251":491,"_291":488,"_292":20,"_117":492},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[495],{"_251":496,"_291":493,"_292":20,"_117":326},"edge","github.com",[499,500],{"_251":503,"_291":497,"_292":20,"_117":502},{"_251":501,"_291":497,"_292":20,"_117":502},"logged_in","This domain is owned by Github. 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The data is aggregated and analyzed; therefore, it remains anonymous upon use.\n","nvHeadless","This cookie is likely used to track if a user is using the site in a 'headless' non-GUI mode, which could be for accessibility reasons or for automated processes.","_biz_nA","C0002","Performance Cookies","These cookies are used to provide quantitative measures of our website visitors, such as the number of times you visit, time on page, your mouse movements, scrolling, clicks and keystroke activity on the websites; other browsing, search, or product research behavior; and what brought you to our site. 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 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