NVIDIA Model Card

EDM-Chaos Overview

cBottle

Description:

cBottle is an diffusion model that generates atmospheric states at kilometer resolution using a cascaded diffusion architecture.

This model is for research and development only.

License/Terms of Use:

Use of this model is governed by the NVIDIA Software and Model Evaluation License Agreement.

Deployment Geography:

Global

Use Case:

Researchers and developers in the field of climate modeling and Earth system science would use this model to generate random images of the Earth's atmosphere, leveraging the techniques described in the paper 'Climate in a Bottle: A generative foundation model for the kilometer-scale atmosphere'.

Release Date:

NGC: 05/12/2025

Model Architecture:

Architecture Type:

Convolutional Neural Network (CNN)

Network Architecture:

Song-UNet on HEALPix geometry

cBottle-3d has 150M parameters.
cBottle-video has 282M parameters.
cBottle-SR has 330M parameters.
cBottle-3d-tc adds only two convolutional layers to the lowest level of the UNet in cBottle-3d, has 150M parameters.

Input:

Input Type(s):

Tensor (dataset label, one-hot encoded)
Tensor (day of year)
Tensor (second of day)
Tensor (monthly mean sea surface temperature, SST)
Tensor (Torpical Cyclone location map, at HPX8 resolution, if using TC guidance)

Input Format(s):

PyTorch Tensor
PyTorch Tensor
PyTorch Tensor
PyTorch Tensor
PyTorch Tensor

Input Parameters:

Tensor: 2D (batch, time_window)
Tensor: 2D (batch, time_window)
Tensor: 4D (batch, num_in_channels, time_window, cell)
Tensor: 4D (batch, 1024)
Tensor: 4D (batch, 1024)

Other Properties Related to Input:

dataset label. ERA5 = 1, ICON =0.
day of year in days (0-365)
second of day in seconds (0-86399)
Monthly mean SST input is on the HEALPix 64 Grid
TC location map on HPX8 Grid

Output:

Output Type(s):

Tensor

Output Format:

PyTorch Tensor

Output Parameters:

Four dimensional (batch, channel, time window, cell)

Other Properties Related to Output:

Coarse model outputs to HPX 64 grid
SR model outputs to HPX 1024 grid.

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 training and inference times compared to CPU-only solutions.

Software Integration (Required For NVIDIA Models Only):

Runtime Engine(s):

PyTorch

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Blackwell
NVIDIA Hopper
NVIDIA Turing

[Preferred/Supported] Operating System(s):

['Linux']

Model Version(s):

Model version: v1.2

Training, Testing, and Evaluation Datasets:

Total Number of Datasets:

4 Datasets:

ICON Cycle 3
inputs4MIPs
ERA5
IBTrACS

Dataset Partition:

Training:

ICON: 2020-01-20 03:00:00 through 2024-03-06 12:00:00 (inclusive)
ERA5/inputs4MIPS/IBTrACS: Years 1980--2017 (inclusive)

Validation:

ICON: 2020-01-20 03:00:00 through 2024-03-06 12:00:00 (inclusive)
ERA5/inputs4MIPs/IBTrACS: Years 2018

Evaluation

ICON: no independent data withheld for evaluation
ERA5/inputs4MIPS/IBTrACS: all other years before 1980 and after 2018.

Data Processing Description:

ERA5 For pre-processing, we retrieved hourly ERA5 data for 1980--2018 (inclusive) via the data lake at the National Energy Research Computing Center (NERSC) at 0.25 degree resolution on the lat-lon grid. The 3d atmospheric states are available on pressure levels. We then regridded this using bilinear interpolation to the HEALPix grid with Nside=256. For training the coarse-resolution models, we coarsened ERA5 by pooling to Nside=64 and transformed to zarr format.

ICON We obtained O(PB) of ICON data in zarr format from MPI-M. This dataset\nwas stored on the HEALPix grid with Nside = 1024. Prior to our handling of the data it was\ninterpolated using nearest neighbors from the native icosahedral grid. Five years\nof this data are available at 3-hour (3D) and 30-minute (2D) resolution in time.\nConveniently, this data-set featured pre-coarsened data (again using averaging\npooling) at our coarse-resolution of Nside = 64. We interpolated this coarse\ndata to fixed pressure levels using linear interpolation in the vertical direction.\nTo fill in values at pressure level locations that are below the surface, we use\nextrapolation based on hydrostatic balance constraints and an assumption of a\nconstant temperature lapse rate of 6.5 K/km for temperature and geo-potential.\nFor all other variables, we use constant extrapolation down from the surface.\nThis procedure approximately reproduces ERA5’s undocumented procedure for\nfilling-in below-surface levels.

Public Datasets:

ERA5
ICON
inputs4MIPs (tosbcs)
IBTrACS

Training, Testing, and Evaluation Datasets:

The following datasets were used for Training, Testing, and Evaluation:

Link:

ERA5: https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5
ICON Cycle 3: https://www.wdc-climate.de/ui/entry?acronym=nextGEMS_cyc3
inputs4MIP: https://pcmdi.llnl.gov/mips/input4MIPs/
IBTrACS: https://www.ncei.noaa.gov/products/international-best-track-archive

Data Collection Method by dataset

Automatic/sensors

Labeling Method by dataset

Automatic/sensors

Properties (Quantity, Dataset Descriptions, Sensor(s)):

ERA

ERA5 is a commonly used dataset that represent the best guess of the global atmospheric state at a coarse resolution of 25 km. Briefly, it is created by running a data assimilation algorithm that incorporates observations into the European Center for Medium Range Weather Forecasting's (ECMWF) numerical model. However, it is not available at a high enough resolution in space or time to resolve meso-scale motions on kilometer scale, and in particular deep convection. The ICON simulation fills in this gap since it is available at a global 5km resolution, but is not paired with reality. See Tab. \\ref{tab:datasets} for a detailed overview of the datasets. We retreived data from 1980--2018.

ICON Cycle 3\nThe ICON data is a free-running simulation with the ICON atmospheric model coupled to a dynamic ocean and land. The atmospheric component solves the nonhydrostatic fluid mechanics equations on a global ico-sahedral mesh with a resolution of around 5 km. Unlike ERA5 the model explicitly resolves certain convective motions so a convection parameterization is not used. So even beyond the simple increase in resolution, we expect this to increase the fidelity of the precipitation and cloud fields in this dataset relative to ERA5 where these processes are parameterized. Furthermore, because the ocean and land are dynamically coupled to the atmosphere, we expect this run to obey conservation of heat, momentum and moisture between these different components, unlike in the ERA5 where the observations (rather than conservations) are king.

Testing Dataset:

Same details as Training Dataset. This period was reserved for tuning the model hyperparameters

ICON: 2020-01-20 03:00:00 through 2024-03-06 12:00:00 (inclusive)
ERA5/inputs4MIPs: Years 2018

Evaluation Dataset:

ICON: no independent data withheld for evaluation
ERA5/inputs4MIPS: all other years before 1980 and after 2018.

Inference:

Engine:

PyTorch

Test Hardware:

A100, H100

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Please report security vulnerabilities or NVIDIA AI Concerns here.

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ERA5 = 1, ICON =0. \n- day of year in days (0-365)\n- second of day in seconds (0-86399)\n- Monthly mean SST input is on the HEALPix 64 Grid\n- TC location map on HPX8 Grid\n\n\n\n## Output:\n\n### Output Type(s):\n\n- Tensor\n\n### Output Format:\n\n- PyTorch Tensor\n\n### Output Parameters:\n\n- Four dimensional (batch, channel, time window, cell)\n\n### Other Properties Related to Output:\n\n- Coarse model outputs to HPX 64 grid\n- SR model outputs to HPX 1024 grid.\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 training and inference times compared to CPU-only solutions.\n\n## Software Integration (Required For NVIDIA Models Only):\n\n### Runtime Engine(s):\n\nPyTorch\n\n### Supported Hardware Microarchitecture Compatibility:\n\n- NVIDIA Ampere\n- NVIDIA Blackwell\n- NVIDIA Hopper\n- NVIDIA Turing\n\n### [Preferred/Supported] Operating System(s):\n\n['Linux']\n\n## Model Version(s):\n\n- **Model version**: v1.2\n\n## Training, Testing, and Evaluation Datasets:\n\n#### Total Number of Datasets:\n\n4 Datasets:\n\n- ICON Cycle 3\n- inputs4MIPs\n- ERA5\n- IBTrACS\n\n##### Dataset Partition:\n\n##### Training:\n\n- ICON: 2020-01-20 03:00:00 through 2024-03-06 12:00:00 (inclusive)\n- ERA5/inputs4MIPS/IBTrACS: Years 1980--2017 (inclusive)\n\n##### Validation:\n\n- ICON: 2020-01-20 03:00:00 through 2024-03-06 12:00:00 (inclusive)\n- ERA5/inputs4MIPs/IBTrACS: Years 2018\n\n##### Evaluation\n\n- ICON: no independent data withheld for evaluation\n- ERA5/inputs4MIPS/IBTrACS: all other years before 1980 and after 2018.\n\n#### Data Processing Description:\n\n\n**ERA5** For pre-processing, we retrieved hourly ERA5 data for 1980--2018 (inclusive) via the data lake at the National Energy Research Computing Center (NERSC) at 0.25 degree resolution on the lat-lon grid. The 3d atmospheric states are available on pressure levels. We then regridded this using bilinear interpolation to the HEALPix grid with Nside=256. For training the coarse-resolution models, we coarsened ERA5 by pooling to Nside=64 and transformed to zarr format.\n\n**ICON** We obtained O(PB) of ICON data in zarr format from MPI-M. This dataset\nwas stored on the HEALPix grid with Nside = 1024. Prior to our handling of the data it was\ninterpolated using nearest neighbors from the native icosahedral grid. Five years\nof this data are available at 3-hour (3D) and 30-minute (2D) resolution in time.\nConveniently, this data-set featured pre-coarsened data (again using averaging\npooling) at our coarse-resolution of Nside = 64. We interpolated this coarse\ndata to fixed pressure levels using linear interpolation in the vertical direction.\nTo fill in values at pressure level locations that are below the surface, we use\nextrapolation based on hydrostatic balance constraints and an assumption of a\nconstant temperature lapse rate of 6.5 K/km for temperature and geo-potential.\nFor all other variables, we use constant extrapolation down from the surface.\nThis procedure approximately reproduces ERA5’s undocumented procedure for\nfilling-in below-surface levels. \n\n#### Public Datasets:\n\n- ERA5\n- ICON\n- inputs4MIPs (tosbcs)\n- IBTrACS\n\n#### Training, Testing, and Evaluation Datasets:\n\nThe following datasets were used for Training, Testing, and Evaluation:\n\n#### Link:\n\n- ERA5: https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5\n- ICON Cycle 3: https://www.wdc-climate.de/ui/entry?acronym=nextGEMS_cyc3\n- inputs4MIP: https://pcmdi.llnl.gov/mips/input4MIPs/ \n- IBTrACS: https://www.ncei.noaa.gov/products/international-best-track-archive\n\n#### Data Collection Method by dataset\n\nAutomatic/sensors\n\n#### Labeling Method by dataset\n\nAutomatic/sensors\n\n#### Properties (Quantity, Dataset Descriptions, Sensor(s)):\n\n**ERA**\n\nERA5 is a commonly used dataset that represent the best guess of the global atmospheric state at a coarse resolution of 25 km. Briefly, it is created by running a data assimilation algorithm that incorporates observations into the European Center for Medium Range Weather Forecasting's (ECMWF) numerical model. However, it is not available at a high enough resolution in space or time to resolve meso-scale motions on kilometer scale, and in particular deep convection. The ICON simulation fills in this gap since it is available at a global 5km resolution, but is not paired with reality. See Tab. \\ref{tab:datasets} for a detailed overview of the datasets. We retreived data from 1980--2018.\n\n\n**ICON Cycle 3**\nThe ICON data is a free-running simulation with the ICON atmospheric model coupled to a dynamic ocean and land. The atmospheric component solves the nonhydrostatic fluid mechanics equations on a global ico-sahedral mesh with a resolution of around 5 km. Unlike ERA5 the model explicitly resolves certain convective motions so a convection parameterization is not used. So even beyond the simple increase in resolution, we expect this to increase the fidelity of the precipitation and cloud fields in this dataset relative to ERA5 where these processes are parameterized. Furthermore, because the ocean and land are dynamically coupled to the atmosphere, we expect this run to obey conservation of heat, momentum and moisture between these different components, unlike in the ERA5 where the observations (rather than conservations) are king.\n\n#### Testing Dataset:\n\nSame details as Training Dataset. This period was reserved for tuning the model hyperparameters\n\n- ICON: 2020-01-20 03:00:00 through 2024-03-06 12:00:00 (inclusive)\n- ERA5/inputs4MIPs: Years 2018\n\n\n#### Evaluation Dataset:\n\n- ICON: no independent data withheld for evaluation\n- ERA5/inputs4MIPS: all other years before 1980 and after 2018.\n\n## Inference:\n\n### Engine:\n\nPyTorch\n\n### Test Hardware:\n\n- A100, H100\n\n\n## Ethical Considerations:\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.\n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","Climate in a Bottle",["D",1755554885533],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/earth-2/models/cbottle","rawImageUrl","https://gitlab-master.nvidia.com/nbrenowitz/edm-chaos/-/wikis/uploads/0ce64cd2604dad5640b503a93a2bea13/tas.jpeg","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[254],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_223":255,"_256":255,"_257":258,"_259":260},"NSPECT-VQQY-A552","value","filter",{"_261":260,"_256":255},"category","label","field",{"_133":1433,"_135":1434},"ngcApps",[1386],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1368":1369,"_1370":1371,"_1372":1373,"_1374":1375,"_1376":273,"_160":297,"_1377":1378,"_1366":59,"_1379":91,"_1380":91,"_1381":1382,"_1383":91,"_1384":1385},"amplitude",{"_1364":1365,"_1366":59,"_968":-7,"_1367":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1358":1359,"_1360":1361,"_1362":1363},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.4.0","defaultSearchSuggestions",{"_1243":191,"_1244":1245,"_1246":1247,"_1248":1249},"announcements",[],"consentState",{"_334":1233,"_1234":91,"_1235":35,"_1236":35,"_1237":1238},"consentConfig",{"_316":317,"_318":319,"_320":321,"_322":323,"_324":325,"_326":327,"_328":329,"_330":331,"_332":333,"_334":335},"showContactAdmin",1783314747746,[],[],[311,312,313,314,315],{"_49":62,"_51":73,"_53":128,"_55":-5,"_57":-5,"_58":59},{"_49":62,"_51":69,"_53":128,"_55":-5,"_57":-5,"_58":59},{"_49":62,"_51":63,"_53":128,"_55":-5,"_57":-5,"_58":59},{"_49":60,"_51":61,"_53":128,"_55":-5,"_57":-5,"_58":59},{"_49":50,"_51":52,"_53":128,"_55":-5,"_57":-5,"_58":59},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",[484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518],[357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373],{"_374":480,"_223":480,"_190":191,"_348":481},{"_374":475,"_223":475,"_190":191,"_348":476},{"_374":471,"_223":471,"_190":191,"_348":472},{"_374":466,"_223":466,"_190":191,"_348":467},{"_374":461,"_223":461,"_190":191,"_348":462},{"_374":449,"_223":449,"_190":191,"_348":450},{"_374":437,"_223":437,"_190":191,"_348":438},{"_374":432,"_223":432,"_190":191,"_348":433},{"_374":421,"_223":421,"_190":191,"_348":422},{"_374":416,"_223":416,"_190":191,"_348":417},{"_374":411,"_223":411,"_190":191,"_348":412},{"_374":406,"_223":406,"_190":191,"_348":407},{"_374":401,"_223":401,"_190":191,"_348":402},{"_374":396,"_223":396,"_190":191,"_348":397},{"_374":391,"_223":391,"_190":191,"_348":392},{"_374":384,"_223":384,"_190":191,"_348":385},{"_374":375,"_223":375,"_190":191,"_348":376},"hostName","amazon-adsystem.com",[377,378],{"_80":383,"_380":375,"_381":35,"_190":382},{"_80":379,"_380":375,"_381":35,"_190":382},"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",[386,387],{"_80":390,"_380":384,"_381":35,"_190":389},{"_80":388,"_380":384,"_381":35,"_190":389},"IDE","This domain is owned by Doubleclick (Google). The main business activity is: Doubleclick is Googles real time bidding advertising exchange","test_cookie","bing.com",[393],{"_80":394,"_380":391,"_381":35,"_190":395},"MUID","This domain is owned by Mircosoft - it is the site for the search engine Bing.","assets.adobedtm.com",[398],{"_80":399,"_380":396,"_381":35,"_190":400},"kndctr_F207D74D549850760A4C98C6_AdobeOrg_consent","If you know anything about this host and its uses please add your contributions here. ","tiktok.com",[403],{"_80":404,"_380":401,"_381":35,"_190":405},"_ttp","TikTok is a social media platform owned by ByteDance, known for short-form video sharing. The service is used to create and share videos, and it utilizes cookies for various purposes including advertising and analytics.","t.co",[408],{"_80":409,"_380":406,"_381":35,"_190":410},"muc_ads","This domain is owned by Twitter. It is used as a URL shortening service, which allows Twitter to collect data on how links are shared.","snowplow.com",[413],{"_80":414,"_380":411,"_381":35,"_190":415},"sp","Testing","google.com",[418],{"_80":419,"_380":416,"_381":35,"_190":420},"NID","This domain is owned by Google Inc. Although Google is primarily known as a search engine, the company provides a diverse range of products and services. Its main source of revenue however is advertising. Google tracks users extensively both through its own products and sites, and the numerous technologies embedded into many millions of websites around the world. It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","tags.srv.stackadapt.com",[423,424,425,426],{"_80":431,"_380":421,"_381":35,"_190":428},{"_80":430,"_380":421,"_381":35,"_190":428},{"_80":429,"_380":421,"_381":35,"_190":428},{"_80":427,"_380":421,"_381":35,"_190":428},"sa-user-id-v3","This domain is associated with StackAdapt, a platform for programmatic advertising, which suggests that it is used for targeting and advertising purposes.","sa-user-id","sa-user-id-v2","sa-user-id-v4","linkedin.com",[434],{"_80":435,"_380":432,"_381":35,"_190":436},"bcookie","This domain is owned by LinkedIn, the business networking platform. It typically acts as a third party host where website owners have placed one of its content sharing buttons in their pages, although its content and services can be embedded in other ways. Although such buttons add functionality to the website they are on, cookies are set regardless of whether or not the visitor has an active Linkedin profile, or agreed to their terms and conditions. For this reason it is classified as a primarily tracking/targeting domain.","twitter.com",[439,440,441,442],{"_80":448,"_380":437,"_381":35,"_190":444},{"_80":446,"_380":437,"_381":35,"_190":447},{"_80":445,"_380":437,"_381":35,"_190":444},{"_80":443,"_380":437,"_381":35,"_190":444},"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",[451,452,453,454],{"_80":460,"_380":449,"_381":35,"_190":458},{"_80":459,"_380":449,"_381":35,"_190":458},{"_80":457,"_380":449,"_381":35,"_190":458},{"_80":455,"_380":449,"_381":35,"_190":456},"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. 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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,[607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627],[572,573,574,575,576],{"_374":466,"_223":466,"_190":191,"_348":598},{"_374":432,"_223":432,"_190":191,"_348":593},{"_374":586,"_223":586,"_190":191,"_348":587},{"_374":582,"_223":582,"_190":191,"_348":583},{"_374":577,"_223":577,"_190":191,"_348":578},"nvcrm.my.site.com",[579],{"_80":580,"_380":577,"_381":35,"_190":581},"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",[584],{"_80":585,"_380":582,"_381":35,"_190":415},"edge","github.com",[588,589],{"_80":592,"_380":586,"_381":35,"_190":591},{"_80":590,"_380":586,"_381":35,"_190":591},"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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