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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

Corrector Diffusion (CorrDiff) US GEFS-HRRR model down-scales several surface and atmospheric variables from 25-km resolution\nforecast data from the Global Ensemble Forecast System (GEFS) and predicts 3-km\nresolution High-Resolution Rapid Refresh (HRRR) data. CorrDiff US allows the prediction of high-fidelity stochastic weather phenomena over\nthe CONUS from low-fidelity input data that would otherwise require expensive regional\nnumerical simulations.

CorrDiff is a generative downscaling model trained over the contiguous United States (CONUS).

This model is ready for commercial use.

Reference(s)

Model Architecture

Architecture Type: Diffusion
\nNetwork Architecture: Patch-Based Corrector Diffusion

Input

Input Type(s):

Tensor (38 Surface & Atmospheric Variables + Forecast Lead Time)
Input data forecast lead time in hours

Input Format(s): NumPy
\nInput Parameters:

Four Dimensional (4D) (batch, variable, latitude, longitude)
Integer (Lead Time in Hours)

Other Properties Related to Input:

0.25 degree latitude-longitude grid bounded over CONUS
Input resolution: [129, 301]
Latitude Coordinates: [53, 52.75, 52.5, ..., 21.5, 21.25, 21]
Longitude Coordinates: [225, 225.25, 225.5, ..., 299.5, 299.75, 300]
Input weather variables: \"u10m\", \"v10m\", \"t2m\", \"r2m\", \"sp\", \"msl\",\"tcwv\", \"u1000\", \"u925\", \"u850\", \"u700\", \"u500\", \"u250\", \"v1000\", \"v925\", \"v850\", \"v700\", \"v500\", \"v250\", \"z1000\", \"z925\", \"z850\", \"z700\", \"z500\", \"z200\", \"t1000\", \"t925\", \"t850\", \"t700\", \"t500\", \"t100\", \"r1000\", \"r925\", \"r850\", \"r700\", \"r500\", \"r100\"

Output

Output Type(s): Tensor (8 Surface & Atmospheric Variables)
\nOutput Format: NumPy
\nOutput Parameters: 5D (batch, samples, variable, latitude, longitude)
\nOther Properties Related to Output:

3-km lambert-conformal projection over CONUS of resolution
Output resolution: [1056, 1792]
Output weather variables: \"u10m\", \"v10m\", \"t2m\", \"tp\", \"csnow\", \"cicep\", \"cfrzr\", \"crain\"

The output is on a cropped window of the grid used by HRRR.\nRefer to the HRRR documentation for additional information on this grid.\nThe output coordinates can be obtained from the corrdiff_output_lat.npy and corrdiff_output_lon.npy\nfiles in the model package.

Software Integration

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

NVIDIA Ampere
NVIDIA Hopper
NVIDIA Turing

Supported Operating System(s):

Linux

Model Version(s)

Model version: v1

Training, Testing, and Evaluation Datasets:

Training Dataset

Link: GEFS

Data Collection Method by dataset

Automatic/Sensors

Labeling Method by dataset

Automatic/Sensors

Properties (Quantity, Dataset Descriptions, Sensor(s)):\nGEFS data for the date range of 2020/12/02 to 2023/12/31. The Global Ensemble Forecast System (GEFS) is a weather model created by the National Centers for Environmental Prediction (NCEP) that generates 21 separate forecasts (ensemble members) to address underlying uncertainties in the input data such limited coverage, instruments or observing systems biases, and the limitations of the model itself.

Link: HRRR

Data Collection Method by dataset

Automatic/Sensors

Labeling Method by dataset

Automatic/Sensors

Properties (Quantity, Dataset Descriptions, Sensor(s)):\nHRRR data for the date range of 2020/12/02 to 2023/12/31. The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation.

Evaluation Dataset

Link: GEFS

Data Collection Method by dataset

Automatic/Sensors

Labeling Method by dataset

Automatic/Sensors

Properties (Quantity, Dataset Descriptions, Sensor(s)):\nGEFS data for the date range of 2024/01/01 to 2024/07/31. The Global Ensemble Forecast System (GEFS) is a weather model created by the National Centers for Environmental Prediction (NCEP) that generates 21 separate forecasts (ensemble members) to address underlying uncertainties in the input data such limited coverage, instruments or observing systems biases, and the limitations of the model itself.

Link: HRRR

Data Collection Method by dataset

Automatic/Sensors

Labeling Method by dataset

Automatic/Sensors

Properties (Quantity, Dataset Descriptions, Sensor(s)):\nHRRR data for the date range of 2024/01/01 to 2024/07/31. The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation.

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

The NIM container is governed by the NVIDIA AI Product Agreement; and the use of this model is governed by the NVIDIA AI Foundation Models Community License. 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","versionNotFound","isLoggedIn","latestVersionPath","/orgs/nim/nvidia/models/earth2-corrdiff-us-gefs-hrrr/-","version","-","imageReference","cliDownloadCommand","ngc registry model download-version nim/nvidia/earth2-corrdiff-us-gefs-hrrr:1.0.0","helmFetchCommand","viewAllVersionsPath","/orgs/nim/nvidia/models/earth2-corrdiff-us-gefs-hrrr/-/version-history","accessCheckResult",{"_105":106,"_107":108,"_109":20},"hasRelatedCollections","context",{"_101":102,"_103":104},1785730028198,[],[],[94,95,96,97,98,99],{"_33":48,"_35":58,"_37":50,"_39":51,"_41":59,"_43":44},{"_33":48,"_35":55,"_37":38,"_39":56,"_41":57,"_43":44},{"_33":48,"_35":53,"_37":38,"_39":56,"_41":100,"_43":44},{"_33":48,"_35":49,"_37":50,"_39":51,"_41":52,"_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},215,"orgName","nim","teamName","nvidia","reason","subscription_required","requiredProducts",[110,111,112],"allowed","earth-2-inference","nim-dev","nv-ai-enterprise","id","nim/nvidia/earth2-corrdiff-us-gefs-hrrr","key","nim/nvidia/models/earth2-corrdiff-us-gefs-hrrr","resourceType","models","resourceId","earth2-corrdiff-us-gefs-hrrr","shortDescription","Corrector Diffusion (CorrDiff) US GEFS-HRRR is a generative downscaling model for the contiguous United States.","description","## Model Overview\n\n### Description\n\nCorrector Diffusion (CorrDiff) US GEFS-HRRR model down-scales several surface and atmospheric variables from 25-km resolution\nforecast data from the Global Ensemble Forecast System (GEFS) and predicts 3-km\nresolution High-Resolution Rapid Refresh (HRRR) data. CorrDiff US allows the prediction of high-fidelity stochastic weather phenomena over\nthe CONUS from low-fidelity input data that would otherwise require expensive regional\nnumerical simulations.\n\nCorrDiff is a generative downscaling model trained over the contiguous United States (CONUS).\n\nThis model is ready for commercial use.\n\n### Reference(s)\n\n* [CorrDiff Paper](https://arxiv.org/pdf/2309.15214)
\n* [Codebase](https://github.com/NVIDIA/modulus)
\n* [Global Ensemble Forecast System](https://www.ncei.noaa.gov/products/weather-climate-models/global-ensemble-forecast)
\n* [The High-Resolution Rapid Refresh](https://rapidrefresh.noaa.gov/hrrr/)
\n\n### Model Architecture\n\n**Architecture Type:** Diffusion
\n**Network Architecture:** Patch-Based Corrector Diffusion
\n\n### Input\n\n**Input Type(s):**\n\n- Tensor (38 Surface & Atmospheric Variables + Forecast Lead Time)
\n- Input data forecast lead time in hours
\n\n**Input Format(s):** NumPy
\n**Input Parameters:**\n\n- Four Dimensional (4D) (batch, variable, latitude, longitude)
\n- Integer (Lead Time in Hours)
\n\n**Other Properties Related to Input:**\n- 0.25 degree latitude-longitude grid bounded over CONUS\n- Input resolution: [129, 301]\n- Latitude Coordinates: [53, 52.75, 52.5, ..., 21.5, 21.25, 21]\n- Longitude Coordinates: [225, 225.25, 225.5, ..., 299.5, 299.75, 300]\n- Input weather variables: \"u10m\", \"v10m\", \"t2m\", \"r2m\", \"sp\", \"msl\",\"tcwv\", \"u1000\", \"u925\", \"u850\", \"u700\", \"u500\", \"u250\", \"v1000\", \"v925\", \"v850\", \"v700\", \"v500\", \"v250\", \"z1000\", \"z925\", \"z850\", \"z700\", \"z500\", \"z200\", \"t1000\", \"t925\", \"t850\", \"t700\", \"t500\", \"t100\", \"r1000\", \"r925\", \"r850\", \"r700\", \"r500\", \"r100\"
\n\n### Output\n\n**Output Type(s):** Tensor (8 Surface & Atmospheric Variables)
\n**Output Format:** NumPy
\n**Output Parameters:** 5D (batch, samples, variable, latitude, longitude)
\n**Other Properties Related to Output:**\n- 3-km lambert-conformal projection over CONUS of resolution\n- Output resolution: [1056, 1792]\n- Output weather variables: \"u10m\", \"v10m\", \"t2m\", \"tp\", \"csnow\", \"cicep\", \"cfrzr\", \"crain\"
\n\nThe output is on a cropped window of the grid used by HRRR.\nRefer to the [HRRR documentation](https://rapidrefresh.noaa.gov/hrrr/) for additional information on this grid.\nThe output coordinates can be obtained from the `corrdiff_output_lat.npy` and `corrdiff_output_lon.npy`\nfiles in the model package.\n\n### Software Integration\n\n**Runtime Engine(s):** Not Applicable
\n**Supported Hardware Microarchitecture Compatibility:**
\n* NVIDIA Ampere
\n* NVIDIA Hopper
\n* NVIDIA 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:** [GEFS](https://www.ncei.noaa.gov/products/weather-climate-models/global-ensemble-forecast)
\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)):**\nGEFS data for the date range of 2020/12/02 to 2023/12/31. The Global Ensemble Forecast System (GEFS) is a weather model created by the National Centers for Environmental Prediction (NCEP) that generates 21 separate forecasts (ensemble members) to address underlying uncertainties in the input data such limited coverage, instruments or observing systems biases, and the limitations of the model itself.
\n\n**Link:** [HRRR](https://rapidrefresh.noaa.gov/hrrr/)
\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)):**\nHRRR data for the date range of 2020/12/02 to 2023/12/31. The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation.
\n\n### Evaluation Dataset\n\n**Link:** [GEFS](https://www.ncei.noaa.gov/products/weather-climate-models/global-ensemble-forecast)
\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)):**\nGEFS data for the date range of 2024/01/01 to 2024/07/31. The Global Ensemble Forecast System (GEFS) is a weather model created by the National Centers for Environmental Prediction (NCEP) that generates 21 separate forecasts (ensemble members) to address underlying uncertainties in the input data such limited coverage, instruments or observing systems biases, and the limitations of the model itself.
\n\n**Link:** [HRRR](https://rapidrefresh.noaa.gov/hrrr/)
\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)):**\nHRRR data for the date range of 2024/01/01 to 2024/07/31. The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation.
\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\n\nThe NIM container is governed by the [NVIDIA AI Product Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/); and the use of this model is governed by the [NVIDIA AI Foundation Models Community License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/). 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 CorrDiff US","updatedDate",["D",1731950776332],"publisher","NVIDIA","latestVersion","1.0.0","versions","collectionCounts","imageUrl","/api/image/nim/nvidia/models/earth2-corrdiff-us-gefs-hrrr","rawImageUrl","https://build.nvidia.com/_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fcorrdiff.jpg&w=1080&q=75","canGuestDownload","latestVersionSizeInBytes",4172419547,"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: | Diffusion Model\nIntended User: | Climate and Weather scientists accelerating weather prediction with AI.\nOutput: | Downscaled inputs.\nDescribe how the model works: | Diffusion model progressively adds noise to interpolated input to recreate fine-resolution features.\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 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 fine-resolution features.\nLicensing: | [NVIDIA AI Foundation Models Community License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/)","privacy","Field | Response\n:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------\nGeneratable or reverse engineerable personally-identifiable information (PII)? | None\nWas consent obtained for any personal data used? | Not Applicable (N/A)\nProtected class data used to create this model? | None\nHow often is dataset reviewed? | Before Release\nIs a mechanism in place to honor data subject right of access or deletion of personal data? | No\nIf personal data collected for the development of the model, was it collected directly by NVIDIA? | N/A\nIf personal data collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | N/A\nIf personal data collected for the development of this AI model, was it minimized to only what was required? | N/A\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? | N/A","safetyAndSecurity","Field | Response\n:---------------------------------------------------|:----------------------------------\nModel Application(s): | Regional Weather Downscaling\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. Restrictions enforce dataset access during training, and dataset license constraints adhered to.","featureLabels",[205,206],"displayLabels",[183,184,185,186],"policyLabels",[],"systemLabels",[],"products",[165,166,167],"hasNimMetadata",true,{"_168":110,"_125":181,"_121":181,"_172":182},{"_168":111,"_125":177,"_121":178,"_135":179,"_172":180},{"_168":112,"_125":169,"_121":170,"_135":171,"_172":173},"name","NVIDIA AI Enterprise","Accelerate your AI agent development","https://assets.ngc.nvidia.com/products/catalog/images/product-NVAIE.jpg","supportedFeatures",[174,175,176],"UNIFIED_CATALOG","SHAREABLE_TO_USER_GROUPS","NGC_CONSOLE","NVIDIA Developer Program","Join the Developer Program for access to free tools, support, and tech resources.","https://assets.ngc.nvidia.com/products/catalog/images/product-nim-dev.jpg",[174],"NVIDIA Earth-2 Inference",[],{"_125":201,"_188":202,"_189":203,"_191":192},{"_125":199,"_188":199,"_189":200,"_191":192},{"_125":195,"_188":196,"_189":197,"_191":192},{"_125":187,"_188":187,"_189":190,"_191":192},"PyTorch","value","filter",{"_193":194,"_188":187},"category","label","field","framework","PhysicsNeMo","pltfm_modulus",{"_193":198,"_188":196},"platform","NSPECT-5W2B-K04K",{"_193":192,"_188":199},"Forecasting","uscs_forecasting",{"_193":204,"_188":202},"application",{"_125":212,"_188":212,"_189":213,"_191":210},{"_125":207,"_188":208,"_189":209,"_191":210},"NVIDIA NIM","nimmcro_nvidia_nim",{"_193":211,"_188":208},"feature","nvidia_nim","NVIDIA AI Enterprise Supported",{"_193":192,"_188":212},"appConfig",{"_1184":1185,"_248":1186},"ngcApps",[1137],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1118":1119,"_1120":1121,"_1122":1123,"_1124":1125,"_1126":226,"_77":253,"_1127":1128,"_1129":44,"_1130":164,"_1131":164,"_1132":1133,"_1134":164,"_1135":1136},"amplitude",{"_1115":1116,"_766":-7,"_1117":20},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1109":1110,"_1111":1112,"_1113":1114},"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,"1.10.0","defaultSearchSuggestions",{"_1006":332,"_1007":1008,"_1009":1010,"_1011":1012},"announcements",[],"consentState",{"_290":996,"_997":164,"_998":20,"_999":20,"_1000":1001},"consentConfig",{"_272":273,"_274":275,"_276":277,"_278":279,"_280":281,"_282":283,"_284":285,"_286":287,"_288":289,"_290":291},"showContactAdmin",1785730027733,[],[],[267,268,269,270,271],{"_33":48,"_35":58,"_37":54,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":55,"_37":54,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":49,"_37":54,"_39":-5,"_41":-5,"_43":44},{"_33":45,"_35":46,"_37":54,"_39":-5,"_41":-5,"_43":44},{"_33":34,"_35":36,"_37":38,"_39":40,"_41":42,"_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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For more information about our privacy practices, please see our Privacy Policy.","mainText","Cookie Settings","alertNoticeText",{"_988":989,"_990":991,"_992":993,"_994":995},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[292,293,294,295,296],{"_297":677,"_168":678,"_123":679,"_301":164,"_302":680,"_304":681,"_306":682},{"_297":579,"_168":580,"_123":581,"_301":20,"_302":582,"_304":583,"_306":584},{"_297":487,"_168":488,"_123":489,"_301":20,"_302":42,"_304":490,"_306":491},{"_297":308,"_168":309,"_123":310,"_301":20,"_302":47,"_304":311,"_306":312},{"_297":298,"_168":299,"_123":300,"_301":20,"_302":303,"_304":305,"_306":307},"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.",[429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450],[313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329],{"_330":424,"_125":424,"_123":332,"_304":425},{"_330":420,"_125":420,"_123":332,"_304":421},{"_330":415,"_125":415,"_123":332,"_304":416},{"_330":411,"_125":411,"_123":332,"_304":412},{"_330":406,"_125":406,"_123":332,"_304":407},{"_330":401,"_125":401,"_123":332,"_304":402},{"_330":393,"_125":393,"_123":332,"_304":394},{"_330":385,"_125":385,"_123":332,"_304":386},{"_330":380,"_125":380,"_123":332,"_304":381},{"_330":375,"_125":375,"_123":332,"_304":376},{"_330":370,"_125":370,"_123":332,"_304":371},{"_330":365,"_125":365,"_123":332,"_304":366},{"_330":360,"_125":360,"_123":332,"_304":361},{"_330":353,"_125":353,"_123":332,"_304":354},{"_330":348,"_125":348,"_123":332,"_304":349},{"_330":341,"_125":341,"_123":332,"_304":342},{"_330":331,"_125":331,"_123":332,"_304":333},"hostName","amazon-adsystem.com","",[334,335],{"_168":340,"_337":331,"_338":20,"_123":339},{"_168":336,"_337":331,"_338":20,"_123":339},"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",[343,344],{"_168":347,"_337":341,"_338":20,"_123":346},{"_168":345,"_337":341,"_338":20,"_123":346},"IDE","This domain is owned by Doubleclick (Google). 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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.","linkedin.com",[382],{"_168":383,"_337":380,"_338":20,"_123":384},"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. 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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.",[521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545],[492,493,494,495,496],{"_330":406,"_125":406,"_123":332,"_304":516},{"_330":380,"_125":380,"_123":332,"_304":511},{"_330":506,"_125":506,"_123":332,"_304":507},{"_330":501,"_125":501,"_123":332,"_304":502},{"_330":497,"_125":497,"_123":332,"_304":498},"nvidia.partners",[499],{"_168":500,"_337":497,"_338":20,"_123":374},"edge","app.hushly.com",[503],{"_168":504,"_337":501,"_338":20,"_123":505},"*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",[508],{"_168":509,"_337":506,"_338":20,"_123":510},"_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.",3,[605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633],[585,586,587,588],{"_330":600,"_125":600,"_123":332,"_304":601},{"_330":595,"_125":595,"_123":332,"_304":596},{"_330":393,"_125":393,"_123":332,"_304":592},{"_330":506,"_125":506,"_123":332,"_304":589},[590],{"_168":591,"_337":506,"_338":164,"_123":510},"_gh_sess",[593],{"_168":594,"_337":393,"_338":164,"_123":400},"YSC","bizible.com",[597],{"_168":598,"_337":595,"_338":20,"_123":599},"_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",[602],{"_168":603,"_337":600,"_338":20,"_123":604},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_168":675,"_337":452,"_338":20,"_123":676},{"_168":660,"_337":674,"_338":164,"_123":661},{"_168":643,"_337":455,"_338":20,"_123":567},{"_168":643,"_337":555,"_338":20,"_123":567},{"_168":672,"_337":452,"_338":20,"_123":673},{"_168":660,"_337":458,"_338":164,"_123":661},{"_168":670,"_337":452,"_338":20,"_123":671},{"_168":660,"_337":455,"_338":164,"_123":661},{"_168":668,"_337":554,"_338":20,"_123":669},{"_168":660,"_337":576,"_338":164,"_123":661},{"_168":640,"_337":455,"_338":20,"_123":642},{"_168":666,"_337":452,"_338":164,"_123":667},{"_168":664,"_337":452,"_338":20,"_123":665},{"_168":662,"_337":452,"_338":164,"_123":663},{"_168":660,"_337":550,"_338":164,"_123":661},{"_168":658,"_337":452,"_338":20,"_123":659},{"_168":656,"_337":452,"_338":20,"_123":657},{"_168":654,"_337":452,"_338":164,"_123":655},{"_168":652,"_337":559,"_338":20,"_123":653},{"_168":640,"_337":550,"_338":20,"_123":642},{"_168":650,"_337":452,"_338":20,"_123":651},{"_168":648,"_337":452,"_338":164,"_123":649},{"_168":646,"_337":452,"_338":20,"_123":647},{"_168":644,"_337":452,"_338":20,"_123":645},{"_168":643,"_337":452,"_338":20,"_123":567},{"_168":640,"_337":641,"_338":20,"_123":642},{"_168":638,"_337":452,"_338":20,"_123":639},{"_168":636,"_337":452,"_338":164,"_123":637},{"_168":634,"_337":452,"_338":20,"_123":635},"_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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