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\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\">

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

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Model Overview

Description

FourCastNet V2 uses Spherical Fourier Neural Operator (SFNO) to predict a collection of surface and atmospheric variables such as wind speed, temperature and pressure and is applied to forecasting global atmospheric dynamics.

FourCastNet is a data-driven model that provides accurate short to medium-range global predictions at a time-step size of 6 hours with predictive stability for over a year of simulated time (1,460 steps), while retaining physically plausible dynamics.

This model is ready for commercial use.

Reference(s)

Model Architecture

Architecture Type: Neural Operator
\nNetwork Architecture: FourCastNet SFNO

Input

Input Type(s):

Tensor (73 Surface & Atmospheric Variables)
DateTime

Input Format(s): NumPy
\nInput Parameters:

Four Dimensional (4D) (batch, variable, latitude, longitude)
Input DateTime

Other Properties Related to Input:

0.25 degree latitude-longitude grid
Input resolution: [721, 1440]
Latitude Coordinates: [90, 89.75, 89.5, ..., -89.5, -89.75, -90]
Longitude Coordinates: [0, 0.25, 0.5, ..., 359.25, 359.5, 359.75]
Input weather variables: \"u10m\", \"v10m\", \"u100m\", \"v100m\", \"t2m\", \"sp\", \"msl\", \"tcwv\", \"u50\", \"u100\", \"u150\", \"u200\", \"u250\", \"u300\", \"u400\", \"u500\", \"u600\", \"u700\", \"u850\", \"u925\", \"u1000\", \"v50\", \"v100\", \"v150\", \"v200\", \"v250\", \"v300\", \"v400\", \"v500\", \"v600\", \"v700\", \"v850\", \"v925\", \"v1000\", \"z50\", \"z100\", \"z150\", \"z200\", \"z250\", \"z300\", \"z400\", \"z500\", \"z600\", \"z700\", \"z850\", \"z925\", \"z1000\", \"t50\", \"t100\", \"t150\", \"t200\", \"t250\", \"t300\", \"t400\", \"t500\", \"t600\", \"t700\", \"t850\", \"t925\", \"t1000\", \"q50\", \"q100\", \"q150\", \"q200\", \"q250\", \"q300\", \"q400\", \"q500\", \"q600\", \"q700\", \"q850\", \"q925\", \"q1000\"

Output

Output Type(s):

Tensor (73 Surface & Atmospheric Variables)

Output Format(s): NumPy
\nOutput Parameters:

Four Dimensional (4D) (batch, variable, latitude, longitude)

Other Properties Related to Output:

Time-delta of 6 hours from input array
0.25 degree latitude-longitude grid
Output resolution: [721, 1440]
Latitude Coordinates: [90, 89.75, 89.5, ..., -89.5, -89.75, -90]
Longitude Coordinates: [0, 0.25, 0.5, ..., 359.25, 359.5, 359.75]
Output weather variables: \"u10m\", \"v10m\", \"u100m\", \"v100m\", \"t2m\", \"sp\", \"msl\", \"tcwv\", \"u50\", \"u100\", \"u150\", \"u200\", \"u250\", \"u300\", \"u400\", \"u500\", \"u600\", \"u700\", \"u850\", \"u925\", \"u1000\", \"v50\", \"v100\", \"v150\", \"v200\", \"v250\", \"v300\", \"v400\", \"v500\", \"v600\", \"v700\", \"v850\", \"v925\", \"v1000\", \"z50\", \"z100\", \"z150\", \"z200\", \"z250\", \"z300\", \"z400\", \"z500\", \"z600\", \"z700\", \"z850\", \"z925\", \"z1000\", \"t50\", \"t100\", \"t150\", \"t200\", \"t250\", \"t300\", \"t400\", \"t500\", \"t600\", \"t700\", \"t850\", \"t925\", \"t1000\", \"q50\", \"q100\", \"q150\", \"q200\", \"q250\", \"q300\", \"q400\", \"q500\", \"q600\", \"q700\", \"q850\", \"q925\", \"q1000\"

Software Integration

Runtime Engine(s): Not Applicable
\nSupported Hardware Microarchitecture Compatibility:

Ampere
Hopper
Turing

Supported Operating System(s):

Linux

Model Version(s)

Model version: v1

Training, Testing, and Evaluation Datasets:

Training Dataset

Link: ERA5

Data Collection Method by dataset

Automatic/Sensors

Labeling Method by dataset

Automatic/Sensors

Properties (Quantity, Dataset Descriptions, Sensor(s)):\nERA5 data for the years of 1979-2017. ERA5 provides hourly estimates of various\natmospheric, land, and oceanic climate variables. The data covers the Earth on a 30km\ngrid and resolves the atmosphere at 137 levels.

Evaluation Dataset

Link: ERA5

Data Collection Method by dataset

Automatic/Sensors

Labeling Method by dataset

Automatic/Sensors

Properties (Quantity, Dataset Descriptions, Sensor(s)):\nERA5 data for the year of 2018. ERA5 provides hourly estimates of various atmospheric,\nland, and oceanic climate variables. The data covers the Earth on a 30km grid and\nresolves the atmosphere at 137 levels.

Inference:

Engine: Triton
\nTest Hardware:

A100
H100
L40S
RTX6000

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established\npolicies and practices to enable development for a wide array of AI applications.\nWhen downloaded or used in accordance with our terms of service, developers should work\nwith their supporting model team to ensure this model meets requirements for the\nrelevant industry and use case and addresses unforeseen product misuse.\nFor more detailed information on ethical considerations for this model, please see the\nModel Card++ Explainability, Bias, Safety & Security, and Privacy Subcards here.\nPlease report security vulnerabilities or NVIDIA AI Concerns here.

License

This model is licensed under the NVIDIA AI Product Agreement. By pulling and using this model, you accept the terms and conditions of this license.

You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.

","resourceVersions","resourceVersionsTotal","hasMoreVersions","versionMetadata","version","-","imageReference","cliDownloadCommand","ngc registry model download-version nim/nvidia/earth2-sfno-era5-73ch:1.0.0-finetuned","helmFetchCommand","viewAllVersionsPath","/orgs/nim/nvidia/models/earth2-sfno-era5-73ch/-/version-history","accessCheckResult",{"_101":102,"_103":104,"_105":20},"hasRelatedCollections","context",{"_97":98,"_99":100},1782796578032,[],[],[89,90,91,92,93,94],{"_33":48,"_35":55,"_37":56,"_39":57,"_41":58,"_43":44},{"_33":48,"_35":52,"_37":38,"_39":53,"_41":54,"_43":44},{"_33":48,"_35":51,"_37":38,"_39":53,"_41":96,"_43":44},{"_33":48,"_35":49,"_37":56,"_39":57,"_41":95,"_43":44},{"_33":45,"_35":46,"_37":38,"_39":40,"_41":47,"_43":44},{"_33":34,"_35":36,"_37":38,"_39":40,"_41":42,"_43":44},54,44,"orgName","nim","teamName","nvidia","reason","subscription_required","requiredProducts",[106,107,108],"allowed","earth-2-inference","nim-dev","nv-ai-enterprise","id","nim/nvidia/earth2-sfno-era5-73ch","key","nim/nvidia/models/earth2-sfno-era5-73ch","resourceType","models","resourceId","earth2-sfno-era5-73ch","shortDescription","FourCastNet V2 model for predicting atmospheric dynamics.","description","## Model Overview\n\n### Description\n\nFourCastNet V2 uses Spherical Fourier Neural Operator (SFNO) to predict a collection of surface and atmospheric variables such as wind speed, temperature and pressure and is applied to forecasting global atmospheric dynamics.\n\nFourCastNet is a data-driven model that provides accurate short to medium-range global predictions at a time-step size of 6 hours with predictive stability for over a year of simulated time (1,460 steps), while retaining physically plausible dynamics.\n\nThis model is ready for commercial use.\n\n### Reference(s)\n\n* [Spherical Fourier Neural Operator Paper](https://arxiv.org/abs/2306.03838)
\n* [FourCastNet Paper](https://arxiv.org/abs/2202.11214)
\n* [Codebase](https://github.com/NVIDIA/modulus)
\n* [The ERA5 global reanalysis](https://doi.org/10.1002/qj.3803)
\n\n### Model Architecture\n\n**Architecture Type:** Neural Operator
\n**Network Architecture:** FourCastNet SFNO
\n\n### Input\n\n**Input Type(s):**\n\n- Tensor (73 Surface & Atmospheric Variables)\n- DateTime
\n\n**Input Format(s):** NumPy
\n**Input Parameters:**\n\n- Four Dimensional (4D) (batch, variable, latitude, longitude)
\n- Input DateTime
\n\n**Other Properties Related to Input:**\n- 0.25 degree latitude-longitude grid\n- Input resolution: [721, 1440]\n- Latitude Coordinates: [90, 89.75, 89.5, ..., -89.5, -89.75, -90]\n- Longitude Coordinates: [0, 0.25, 0.5, ..., 359.25, 359.5, 359.75]\n- Input weather variables: \"u10m\", \"v10m\", \"u100m\", \"v100m\", \"t2m\", \"sp\", \"msl\", \"tcwv\", \"u50\", \"u100\", \"u150\", \"u200\", \"u250\", \"u300\", \"u400\", \"u500\", \"u600\", \"u700\", \"u850\", \"u925\", \"u1000\", \"v50\", \"v100\", \"v150\", \"v200\", \"v250\", \"v300\", \"v400\", \"v500\", \"v600\", \"v700\", \"v850\", \"v925\", \"v1000\", \"z50\", \"z100\", \"z150\", \"z200\", \"z250\", \"z300\", \"z400\", \"z500\", \"z600\", \"z700\", \"z850\", \"z925\", \"z1000\", \"t50\", \"t100\", \"t150\", \"t200\", \"t250\", \"t300\", \"t400\", \"t500\", \"t600\", \"t700\", \"t850\", \"t925\", \"t1000\", \"q50\", \"q100\", \"q150\", \"q200\", \"q250\", \"q300\", \"q400\", \"q500\", \"q600\", \"q700\", \"q850\", \"q925\", \"q1000\"
\n\n### Output\n\n**Output Type(s):**\n\n- Tensor (73 Surface & Atmospheric Variables)\n\n**Output Format(s):** NumPy
\n**Output Parameters:**\n\n- Four Dimensional (4D) (batch, variable, latitude, longitude)
\n\n**Other Properties Related to Output:**\n- Time-delta of 6 hours from input array\n- 0.25 degree latitude-longitude grid\n- Output resolution: [721, 1440]\n- Latitude Coordinates: [90, 89.75, 89.5, ..., -89.5, -89.75, -90]\n- Longitude Coordinates: [0, 0.25, 0.5, ..., 359.25, 359.5, 359.75]\n- Output weather variables: \"u10m\", \"v10m\", \"u100m\", \"v100m\", \"t2m\", \"sp\", \"msl\", \"tcwv\", \"u50\", \"u100\", \"u150\", \"u200\", \"u250\", \"u300\", \"u400\", \"u500\", \"u600\", \"u700\", \"u850\", \"u925\", \"u1000\", \"v50\", \"v100\", \"v150\", \"v200\", \"v250\", \"v300\", \"v400\", \"v500\", \"v600\", \"v700\", \"v850\", \"v925\", \"v1000\", \"z50\", \"z100\", \"z150\", \"z200\", \"z250\", \"z300\", \"z400\", \"z500\", \"z600\", \"z700\", \"z850\", \"z925\", \"z1000\", \"t50\", \"t100\", \"t150\", \"t200\", \"t250\", \"t300\", \"t400\", \"t500\", \"t600\", \"t700\", \"t850\", \"t925\", \"t1000\", \"q50\", \"q100\", \"q150\", \"q200\", \"q250\", \"q300\", \"q400\", \"q500\", \"q600\", \"q700\", \"q850\", \"q925\", \"q1000\"
\n\n### Software Integration\n\n**Runtime Engine(s):** Not Applicable
\n**Supported Hardware Microarchitecture Compatibility:**
\n* Ampere
\n* Hopper
\n* Turing
\n\n**Supported Operating System(s):**\n* Linux
\n\n### Model Version(s)\n\n**Model version:** v1
\n\n## Training, Testing, and Evaluation Datasets:\n\n### Training Dataset\n\n**Link:** [ERA5](https://cds.climate.copernicus.eu/)
\n\n*Data Collection Method by dataset*
\n* Automatic/Sensors
\n\n*Labeling Method by dataset*
\n* Automatic/Sensors
\n\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):**\nERA5 data for the years of 1979-2017. ERA5 provides hourly estimates of various\natmospheric, land, and oceanic climate variables. The data covers the Earth on a 30km\ngrid and resolves the atmosphere at 137 levels.
\n\n### Evaluation Dataset\n\n**Link:** [ERA5](https://cds.climate.copernicus.eu/)
\n\n*Data Collection Method by dataset*
\n* Automatic/Sensors
\n\n*Labeling Method by dataset*
\n* Automatic/Sensors
\n\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):**\nERA5 data for the year of 2018. ERA5 provides hourly estimates of various atmospheric,\nland, and oceanic climate variables. The data covers the Earth on a 30km grid and\nresolves the atmosphere at 137 levels.
\n\n## Inference:\n\n**Engine:** [Triton](https://developer.nvidia.com/triton-inference-server)
\n**Test Hardware:**\n* A100
\n* H100
\n* L40S
\n* RTX6000
\n\n### Ethical Considerations:\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established\npolicies and practices to enable development for a wide array of AI applications.\nWhen downloaded or used in accordance with our terms of service, developers should work\nwith their supporting model team to ensure this model meets requirements for the\nrelevant industry and use case and addresses unforeseen product misuse.\nFor more detailed information on ethical considerations for this model, please see the\nModel Card++ Explainability, Bias, Safety & Security, and Privacy Subcards [here](https://ai.nvidia.com).\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n## License\nThis model is licensed under the [NVIDIA AI Product Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/). By pulling and using this model, you accept the terms and conditions of this license.\n\n**You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.**","displayName","Earth-2 FourCastNet SFNO","updatedDate",["D",1731952538989],"publisher","NVIDIA","latestVersion","1.0.0-finetuned","versions","collectionCounts","imageUrl","/api/image/nim/nvidia/models/earth2-sfno-era5-73ch","rawImageUrl","https://blogs.nvidia.com/wp-content/uploads/2024/03/earth-2-startups-1280x680-1.jpg","canGuestDownload","latestVersionSizeInBytes",6873667122,"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 of the Above\nMeasures taken to mitigate against unwanted bias: | None","explainability","Field | Response\n:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nIntended Application & Domain: | Global Weather Forecasting\nModel Type: | Neural Operator\nIntended User: | Climate and Weather scientists accelerating weather prediction with AI.\nOutput: | Global forecast prediction of surface and atmosphere variables.\nDescribe how the model works: | Neural operator auto-regressively predicts a time-series forecast from an initial state.\nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable\nTechnical Limitations: | The model may perform poorly for longer range forecasts and atmospheric data sources different from that in the ERA5 training dataset\nVerified to have met prescribed NVIDIA quality standards: | Yes\nPerformance Metrics: | Accuracy, Throughput and Latency\nPotential Known Risks: | This model may mispredict global atmospheric dynamics.\nLicensing: | [NVIDIA AI Product Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/)","privacy","522 B\nField | Response\n:---------------------------------------------------------------------------------------------------|:---------------\nParticipation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None of the Above\nMeasures taken to mitigate against unwanted bias: | None","safetyAndSecurity","Field | Response\n:---------------------------------------------------|:----------------------------------\nModel Application(s): | Global Weather Forecasting\nDescribe the life critical impact (if present). | None Known\nUse Case Restrictions: | Abide by [NVIDIA AI Product Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/)\nModel and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. 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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,[435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469],[307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323],{"_324":431,"_121":431,"_119":326,"_297":432},{"_324":426,"_121":426,"_119":326,"_297":427},{"_324":422,"_121":422,"_119":326,"_297":423},{"_324":417,"_121":417,"_119":326,"_297":418},{"_324":412,"_121":412,"_119":326,"_297":413},{"_324":400,"_121":400,"_119":326,"_297":401},{"_324":388,"_121":388,"_119":326,"_297":389},{"_324":383,"_121":383,"_119":326,"_297":384},{"_324":372,"_121":372,"_119":326,"_297":373},{"_324":367,"_121":367,"_119":326,"_297":368},{"_324":362,"_121":362,"_119":326,"_297":363},{"_324":357,"_121":357,"_119":326,"_297":358},{"_324":352,"_121":352,"_119":326,"_297":353},{"_324":347,"_121":347,"_119":326,"_297":348},{"_324":342,"_121":342,"_119":326,"_297":343},{"_324":335,"_121":335,"_119":326,"_297":336},{"_324":325,"_121":325,"_119":326,"_297":327},"hostName","amazon-adsystem.com","",[328,329],{"_164":334,"_331":325,"_332":20,"_119":333},{"_164":330,"_331":325,"_332":20,"_119":333},"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",[337,338],{"_164":341,"_331":335,"_332":20,"_119":340},{"_164":339,"_331":335,"_332":20,"_119":340},"IDE","This domain is owned by Doubleclick (Google). 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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",[390,391,392,393],{"_164":399,"_331":388,"_332":20,"_119":395},{"_164":397,"_331":388,"_332":20,"_119":398},{"_164":396,"_331":388,"_332":20,"_119":395},{"_164":394,"_331":388,"_332":20,"_119":395},"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",[402,403,404,405],{"_164":411,"_331":400,"_332":20,"_119":409},{"_164":410,"_331":400,"_332":20,"_119":409},{"_164":408,"_331":400,"_332":20,"_119":409},{"_164":406,"_331":400,"_332":20,"_119":407},"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",[414],{"_164":415,"_331":412,"_332":20,"_119":416},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[419],{"_164":420,"_331":417,"_332":20,"_119":421},"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",[424],{"_164":425,"_331":422,"_332":20,"_119":366},"R","6sc.co",[428],{"_164":429,"_331":426,"_332":20,"_119":430},"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,[558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578],[523,524,525,526,527],{"_324":417,"_121":417,"_119":326,"_297":549},{"_324":383,"_121":383,"_119":326,"_297":544},{"_324":537,"_121":537,"_119":326,"_297":538},{"_324":533,"_121":533,"_119":326,"_297":534},{"_324":528,"_121":528,"_119":326,"_297":529},"nvcrm.my.site.com",[530],{"_164":531,"_331":528,"_332":20,"_119":532},"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",[535],{"_164":536,"_331":533,"_332":20,"_119":366},"edge","github.com",[539,540],{"_164":543,"_331":537,"_332":20,"_119":542},{"_164":541,"_331":537,"_332":20,"_119":542},"logged_in","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.modal.cuda","This cookie name suggests it is related to a modal popup functionality on NVIDIA's website, possibly to remember user preferences or interactions with the modal.","readme_library","docs.api.nvidia.com","_im_ses.1006927","geo_locator","nv.modal.deepstream","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.","_im_id.1006927","_hjUserAttributesHash","docs.nvidia.com","This cookie name is associated with Hotjar which is used to know when any user attribute has changed and needs to be updated.","mbox","Cookie set by Adobe SiteCatalyst analytics software - used for measuring the performance of page content using A/B split testing","nv.modal.cudnn","This cookie name suggests it is related to modal popup handling or user interface preferences on NVIDIA's website, specifically for the cuDNN library section.","readme_language","nv.modal.hpc","This cookie name suggests it is related to a modal pop-up on a website, possibly to control the display of a high-priority cookie notice or other important information to the user.","at_check","Used by Adobe Target to check if cookies are enabled/supported on the browser","_biz_uid","This cookie 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.\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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