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GOVERNING TERMS: Use of this NIM container is governed by the NVIDIA Software License Agreement and Product-Specific Terms for AI Products. Use of this model is governed by the NVIDIA Community Model License. ADDITIONAL INFORMATION: MIT.

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Overview

DiffDock is a generative diffusion model for drug discovery in molecular blind docking.

DiffDock consists of two models: the Score and Confidence models. The Score model generates a series of potential poses for protein-ligand binding by running a reverse diffusion process.

DiffDock does not require any information about a binding pocket. During its diffusion process, the molecule's position relative to the protein, its orientation, and the torsion angles are allowed to change. Running the learned reverse diffusion process transforms a distribution of noisy prior molecule poses to the one learned by the model. As a result, it outputs many sampled poses and ranks them via its confidence model.

Leveraging the same neural-network architecture designed in the original DiffDock by MIT, the model v2.2.0 is trained by NVIDIA using PLINDER and SAIR, a state-of-art dataset of well curated and labeled protein-ligand complexes, which therefore, delivers a much higher accuracy for molecular docking tasks.

The container components are ready for commercial/non-commercial use.\n

Third-Party Community Consideration

This model uses the DiffDock neural network architecture originally designed by MIT. NVIDIA has trained this version (v2.2.0) using NVIDIA-curated datasets (PLINDER and SAIR) to improve accuracy for molecular docking tasks.

License / Terms of Use

GOVERNING TERMS: Use of this NIM container is governed by the NVIDIA Software License Agreement and Product-Specific Terms for AI Products. Use of this model is governed by the NVIDIA Open Model License. Additional Information: MIT.

You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.

Deployment Geography

Global

Use Case

DiffDock empowers researchers and commercial users in Drug Discovery, Computational Chemistry, and Digital Biology to generate accurate protein–ligand binding poses without requiring predefined binding pocket information. Trained on NVIDIA-curated datasets (PLINDER and SAIR) using a robust diffusion-based generative framework, DiffDock delivers high-quality pose predictions suitable for early hit discovery, virtual screening, and structure-based design workflows. The model produces multiple candidate poses and ranks them using an integrated confidence module, enabling reliable prioritization of ligands across large-scale docking pipelines.

Release Date

build.nvidia.com 7/6/2026 via build.nvidia.com
\nNGC 7/6/2026 via catalog.ngc.nvidia.com

Program Classes:

The NIM contains the DiffDock model, including DiffDock inference code, model weights (checkpoint). The model (checkpoint) is pulled automatically at NIM startup.

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

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

Model Name	Use Case	Model Card	How to pull the Model
DiffDock	Predicts the three-dimensional structure of a protein-ligand complex.	Model Card	Automated

Deployment Details:

The DiffDock NIM is deployed by pulling and running the container in an environment with appropriate credentials. For instructions to pull and run, hardware requirements, and NVIDIA GPU support matrix, see DiffDock NIM Docs.

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster inference times compared to CPU-only solutions

References:

@inproceedings{corso2023diffdock,\n    title={DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking}, \n    author = {Corso, Gabriele and Stärk, Hannes and Jing, Bowen and Barzilay, Regina and Jaakkola, Tommi},\n    booktitle={International Conference on Learning Representations (ICLR)},\n    year={2023}\n}\n

Container Version(s):

DiffDock v2.3.0

Security Common Vulnerabilities and Exposures (CVEs)

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

Ethical Considerations:

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

Users are responsible for ensuring the physical properties of model-generated molecules are appropriately evaluated and comply with applicable safety regulations and ethical standards.

Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

Get Help

Getting started with the NIM

Deploying and integrating the NIM is straightforward thanks to our industry standard APIs. Visit the NIM Container page for release documentation, deployment guides and more DiffDock NIM Docs.

NVIDIA Developer Community Forum

For support, Visit the NVIDIA Developer Community Forum

End of Support — \"This artifact is no longer supported. NVIDIA strongly recommends artifacts that are up to date and supported\"

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\n\n### Third-Party Community Consideration\nThis model uses the DiffDock neural network architecture originally designed by MIT. NVIDIA has trained this version (v2.2.0) using NVIDIA-curated datasets (PLINDER and SAIR) to improve accuracy for molecular docking tasks.\n\n\n#### License / Terms of Use\n\nGOVERNING TERMS: Use of this NIM container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/) and [Product-Specific Terms for AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/). Use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). Additional Information: [MIT](https://github.com/gcorso/DiffDock/blob/main/LICENSE).\n\n\n**You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.**\n\n### Deployment Geography\nGlobal\n\n### Use Case\nDiffDock empowers researchers and commercial users in Drug Discovery, Computational Chemistry, and Digital Biology to generate accurate protein–ligand binding poses without requiring predefined binding pocket information. Trained on NVIDIA-curated datasets (PLINDER and SAIR) using a robust diffusion-based generative framework, DiffDock delivers high-quality pose predictions suitable for early hit discovery, virtual screening, and structure-based design workflows. The model produces multiple candidate poses and ranks them using an integrated confidence module, enabling reliable prioritization of ligands across large-scale docking pipelines.\n\n### Release Date\nbuild.nvidia.com 7/6/2026 via [build.nvidia.com](https://build.nvidia.com/mit/diffdock)
\nNGC 7/6/2026 via [catalog.ngc.nvidia.com](https://catalog.ngc.nvidia.com/orgs/nim/teams/mit/containers/diffdock)
\n\n## Program Classes:\nThe NIM contains the DiffDock model, including DiffDock inference code, model weights (checkpoint). The model (checkpoint) is pulled automatically at NIM startup.\n\n| Model Name | Use Case | Model Card | How to pull the Model |\n| :--- |:--: | :---: | :---: |\n| DiffDock | Predicts the three-dimensional structure of a protein-ligand complex. | [Model Card](https://build.nvidia.com/mit/diffdock/modelcard) | Automated |\n\n## Deployment Details: \nThe DiffDock NIM is deployed by pulling and running the container in an environment with appropriate credentials. For instructions to pull and run, hardware requirements, and NVIDIA GPU support matrix, see [DiffDock NIM Docs](https://docs.nvidia.com/nim/bionemo/diffdock/latest/overview.html).\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster inference times compared to CPU-only solutions\n\n### References:\n```\n@inproceedings{corso2023diffdock,\n title={DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking}, \n author = {Corso, Gabriele and Stärk, Hannes and Jing, Bowen and Barzilay, Regina and Jaakkola, Tommi},\n booktitle={International Conference on Learning Representations (ICLR)},\n year={2023}\n}\n```\n## Container Version(s): \n\nDiffDock v2.3.0
\n\n## Security Common Vulnerabilities and Exposures (CVEs)\nPlease review the Security Scanning tab on NGC to view the latest security scan results.\nFor certain open-source vulnerabilities listed in the scan results, NVIDIA provides a response in the form of a Vulnerability Exploitability eXchange (VEX) document. The VEX information can be reviewed and downloaded from the Security Scanning tab.\n\n## Ethical Considerations:\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal developer team to ensure these software components meet requirements for the relevant industry and use case and address unforeseen product misuse.\n\nUsers are responsible for ensuring the physical properties of model-generated molecules are appropriately evaluated and comply with applicable safety regulations and ethical standards.
\n\nPlease report quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://app.intigriti.com/programs/nvidia/nvidiavdp/detail).\n\n# Get Help\n\n## Getting started with the NIM\nDeploying and integrating the NIM is straightforward thanks to our industry standard APIs. Visit the NIM Container page for release documentation, deployment guides and more [DiffDock NIM Docs](https://docs.nvidia.com/nim/bionemo/diffdock/latest/overview.html).\n\n## NVIDIA Developer Community Forum\n\nFor support, Visit the [NVIDIA Developer Community Forum](https://forums.developer.nvidia.com/)\n\n**End of Support** — \"This artifact is no longer supported. NVIDIA strongly recommends artifacts that are up to date and supported\"","displayName","DiffDock",["D",1783667386584],"publisher","MIT","latestVersion","versions",[221,225,217,215,213,131,208,206,219,210,204,223,201],"imageUrl","/api/image/nim/mit/containers/diffdock","rawImageUrl","canGuestDownload","latestVersionSizeInBytes",17047482782,"licenseTerms",{"_339":340,"_341":16,"_342":343,"_344":20,"_345":346},"isMultinodeEnabled","isMultiArchitecture","hasSignedTag","featureLabels",[333],"displayLabels",[275,276,277,278,279,280,281,282,283,284,285,286,287,288],"policyLabels",[],"systemLabels",[268],"products",[],"hasNimMetadata",{"_238":269,"_270":269,"_271":272,"_72":273},"signed images","value","filter",{"_274":273,"_270":269},"system","field",{"_238":330,"_270":331,"_271":332,"_72":76},{"_238":327,"_270":328,"_271":329,"_72":76},{"_238":324,"_270":325,"_271":326,"_72":76},{"_238":320,"_270":321,"_271":322,"_72":76},{"_238":318,"_270":318,"_271":319,"_72":76},{"_238":315,"_270":316,"_271":317,"_72":76},{"_238":312,"_270":313,"_271":314,"_72":76},{"_238":308,"_270":309,"_271":310,"_72":76},{"_238":306,"_270":306,"_271":307,"_72":76},{"_238":302,"_270":303,"_271":304,"_72":76},{"_238":299,"_270":299,"_271":300,"_72":76},{"_238":296,"_270":297,"_271":298,"_72":76},{"_238":293,"_270":294,"_271":295,"_72":76},{"_238":289,"_270":290,"_271":291,"_72":76},"Triton Inference Server","pltfm_triton_inference_server",{"_274":292,"_270":290},"platform","RAPIDS","pltfm_rapids",{"_274":292,"_270":294},"PyTorch Geometric","pltfm_pytorch_geometric",{"_274":292,"_270":297},"PyTorch",{"_274":301,"_270":299},"framework","NVIDIA AI","soln_nvidia_ai",{"_274":305,"_270":303},"technology","NSPECT-9OWX-EW8J",{"_274":76,"_270":306},"Life Sciences","indus_life_sciences",{"_274":311,"_270":309},"industry","Inference","soln_inference",{"_274":305,"_270":313},"Healthcare","indus_healthcare",{"_274":311,"_270":316},"Experimental",{"_274":76,"_270":318},"Drug Discovery","uscs_drug_discovery",{"_274":323,"_270":321},"application","CUDA Toolkit","pltfm_cuda_toolkit",{"_274":292,"_270":325},"CUDA","pltfm_cuda",{"_274":292,"_270":328},"AI","soln_ai",{"_274":305,"_270":331},{"_238":334,"_270":335,"_271":336,"_72":337},"NVIDIA NIM","nimmcro_nvidia_nim",{"_274":338,"_270":335},"feature","nvidia_nim","governingTerms","

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GOVERNING TERMS: The trial service is governed by the NVIDIA API Trial Terms of Service. Use of this model is governed by the NVIDIA Community Model License. ADDITIONAL INFORMATION: MIT.

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For more information about our privacy practices, please see our Privacy Policy.","mainText","Cookie Settings","alertNoticeText",{"_1130":1131,"_1132":1133,"_1134":1135,"_1136":1137},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[433,434,435,436,437],{"_438":819,"_202":820,"_161":821,"_442":18,"_443":822,"_445":823,"_447":824},{"_438":722,"_202":723,"_161":724,"_442":20,"_443":48,"_445":725,"_447":726},{"_438":629,"_202":630,"_161":631,"_442":20,"_443":632,"_445":633,"_447":634},{"_438":449,"_202":450,"_161":451,"_442":20,"_443":452,"_445":453,"_447":454},{"_438":439,"_202":440,"_161":441,"_442":20,"_443":444,"_445":446,"_447":448},"optanonGroupId","C0005","Social Media Cookies","These cookies are set by a range of social media services that we have added to the site to enable you to share our content with your friends and networks. They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. 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,[571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592],[455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471],{"_472":566,"_238":566,"_161":474,"_445":567},{"_472":562,"_238":562,"_161":474,"_445":563},{"_472":557,"_238":557,"_161":474,"_445":558},{"_472":553,"_238":553,"_161":474,"_445":554},{"_472":548,"_238":548,"_161":474,"_445":549},{"_472":543,"_238":543,"_161":474,"_445":544},{"_472":535,"_238":535,"_161":474,"_445":536},{"_472":527,"_238":527,"_161":474,"_445":528},{"_472":522,"_238":522,"_161":474,"_445":523},{"_472":517,"_238":517,"_161":474,"_445":518},{"_472":512,"_238":512,"_161":474,"_445":513},{"_472":507,"_238":507,"_161":474,"_445":508},{"_472":502,"_238":502,"_161":474,"_445":503},{"_472":495,"_238":495,"_161":474,"_445":496},{"_472":490,"_238":490,"_161":474,"_445":491},{"_472":483,"_238":483,"_161":474,"_445":484},{"_472":473,"_238":473,"_161":474,"_445":475},"hostName","amazon-adsystem.com","",[476,477],{"_202":482,"_479":473,"_480":20,"_161":481},{"_202":478,"_479":473,"_480":20,"_161":481},"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",[485,486],{"_202":489,"_479":483,"_480":20,"_161":488},{"_202":487,"_479":483,"_480":20,"_161":488},"IDE","This domain is owned by Doubleclick (Google). 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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. 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.","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,[664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688],[635,636,637,638,639],{"_472":548,"_238":548,"_161":474,"_445":659},{"_472":522,"_238":522,"_161":474,"_445":654},{"_472":649,"_238":649,"_161":474,"_445":650},{"_472":644,"_238":644,"_161":474,"_445":645},{"_472":640,"_238":640,"_161":474,"_445":641},"nvidia.partners",[642],{"_202":643,"_479":640,"_480":20,"_161":516},"edge","app.hushly.com",[646],{"_202":647,"_479":644,"_480":20,"_161":648},"*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",[651],{"_202":652,"_479":649,"_480":20,"_161":653},"_octo","This domain is owned by Github. 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We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","nv_country_code","developer.nvidia.com","This cookie is likely used to store the country code of the visitor to provide localized content and possibly for legal compliance with regional data privacy laws.","_gdxxxxxxxxxxxxx","nvidianews.nvidia.com","docs.api.nvidia.com","research.nvidia.com","readme_library","wcmmode","ai-podcast.nvidia.com","resources.nvidia.com","geo_locator","openedx-language-preference","learn.nvidia.com","doxygen_width","raytracing-docs.nvidia.com","This cookie is likely used to remember the width preferences of a sidebar or interface element within a documentation platform like Doxygen.","_sfid_eb78","This cookie is categorized as Functionality Cookie.","crowdsource.nvidia.com","mbox","Cookie set by Adobe SiteCatalyst analytics software - used for measuring the performance of page content using A/B split testing","readme_language","vid","This cookie name 'vid' is used for tracking the search request made by the user on the 'Find a practitioner' page.","at_check","Used by Adobe Target to check if cookies are enabled/supported on the browser","blogs.nvidia.com","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.","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 performance.",[747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775],[727,728,729,730],{"_472":742,"_238":742,"_161":474,"_445":743},{"_472":737,"_238":737,"_161":474,"_445":738},{"_472":535,"_238":535,"_161":474,"_445":734},{"_472":649,"_238":649,"_161":474,"_445":731},[732],{"_202":733,"_479":649,"_480":18,"_161":653},"_gh_sess",[735],{"_202":736,"_479":535,"_480":18,"_161":542},"YSC","bizible.com",[739],{"_202":740,"_479":737,"_480":20,"_161":741},"_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",[744],{"_202":745,"_479":742,"_480":20,"_161":746},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_202":817,"_479":594,"_480":20,"_161":818},{"_202":802,"_479":816,"_480":18,"_161":803},{"_202":785,"_479":597,"_480":20,"_161":710},{"_202":785,"_479":698,"_480":20,"_161":710},{"_202":814,"_479":594,"_480":20,"_161":815},{"_202":802,"_479":600,"_480":18,"_161":803},{"_202":812,"_479":594,"_480":20,"_161":813},{"_202":802,"_479":597,"_480":18,"_161":803},{"_202":810,"_479":697,"_480":20,"_161":811},{"_202":802,"_479":719,"_480":18,"_161":803},{"_202":782,"_479":597,"_480":20,"_161":784},{"_202":808,"_479":594,"_480":18,"_161":809},{"_202":806,"_479":594,"_480":20,"_161":807},{"_202":804,"_479":594,"_480":18,"_161":805},{"_202":802,"_479":693,"_480":18,"_161":803},{"_202":800,"_479":594,"_480":20,"_161":801},{"_202":798,"_479":594,"_480":20,"_161":799},{"_202":796,"_479":594,"_480":18,"_161":797},{"_202":794,"_479":702,"_480":20,"_161":795},{"_202":782,"_479":693,"_480":20,"_161":784},{"_202":792,"_479":594,"_480":20,"_161":793},{"_202":790,"_479":594,"_480":18,"_161":791},{"_202":788,"_479":594,"_480":20,"_161":789},{"_202":786,"_479":594,"_480":20,"_161":787},{"_202":785,"_479":594,"_480":20,"_161":710},{"_202":782,"_479":783,"_480":20,"_161":784},{"_202":780,"_479":594,"_480":20,"_161":781},{"_202":778,"_479":594,"_480":18,"_161":779},{"_202":776,"_479":594,"_480":20,"_161":777},"_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. 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.","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.","_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.","_session_id","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.","_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. 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