Earth-2 Checkpoints: FourCastNet 3

Description:

FourCastNet 3 advances global weather modeling by implementing a scalable, geometric\nmachine learning (ML) approach to probabilistic ensemble forecasting. The approach is\ndesigned to respect spherical geometry and to accurately model the spatially\ncorrelated probabilistic nature of the problem, resulting in stable spectra and\nrealistic dynamics across multiple scales. FourCastNet 3 delivers forecasting accuracy\nthat surpasses leading conventional ensemble models and rivals the best diffusion-based\nmethods, while producing forecasts 8 to 60 times faster than these approaches. In\ncontrast to other ML approaches, FourCastNet 3 demonstrates excellent probabilistic\ncalibration and retains realistic spectra, even at extended lead times of up to 60 days.\nAll of these advances are realized using a purely convolutional neural network\narchitecture specifically tailored for spherical geometry. Scalable and eﬃcient\nlarge-scale training on 1024 GPUs and more is enabled by a novel training paradigm for\ncombined model- and data-parallelism, inspired by domain decomposition methods in\nclassical numerical models. Additionally, FourCastNet 3 enables rapid inference on a\nsingle GPU, producing a 60-day global forecast at 0.25°, 6-hourly resolution in under\n4 minutes. Its computational efficiency, medium-range probabilistic skill, spectral\nfidelity, and rollout stability at subseasonal timescales make it a strong candidate\nfor improving meteorological forecasting and early warning systems through large\nensemble predictions.

$\"FCN3$

This model is ready for commercial/non-commercial use.

License/Terms of Use:

Apache 2.0 license

Deployment Geography:

Global

Use Case:

Industry, academic, and government research teams interested in medium-range and\nsubseasonal-to-seasonal weather forecasting, and climate modeling.

Release Date:

NGC 07/18/2025

Reference:

Papers:

Code:

\n
Makani
\n
\n
PhysicsNeMo
\n
\n
Earth2Studio
\n
\n
torch-harmonics
\n

Model Architecture:

Architecture Type: Spherical Neural Operator. A fully convolutional architecture\nbased on group convolutions defined on the sphere. Leverages both local and global\nconvolutions. For details regarding the architecture refer to the\nFourCastNet 3 paper.

Network Architecture: N/A

Number of model parameters: 710,867,670

Model datatype: We recommend that the model is run in AMP with bf16, however, the\ninputs and outputs are typically float32.

Input:

Input Type:

Tensor (72 surface and pressure-level variables)

Input Format: PyTorch Tensor
\nInput Parameters:

Six Dimensional (6D) (batch, time, lead time, variable, latitude, longitude)

Other Properties Related to Input:

Input equi-rectangular latitude/longitude grid: 0.25 degree 721 x 1440
Input state weather variables: u10m, v10m, u100m, v100m, t2m, msl,\ntcwv, u50, u100, u150, u200, u250, u300, u400, u500, u600, u700,\nu850, u925, u1000, v50, v100, v150, v200, v250, v300, v400, v500,\nv600, v700, v850, v925, v1000, z50, z100, z150, z200, z250, z300,\nz400, z500, z600, z700, z850, z925, z1000, t50, t100, t150, t200,\nt250, t300, t400, t500, t600, t700, t850, t925, t1000, q50, q100,\nq150, q200, q250, q300, q400, q500, q600, q700, q850, q925, q1000
Time: datetime64

For variable name information, review the Lexicon at Earth2Studio.

Output:

Output Type: Tensor (72 surface and pressure-level variables)
\nOutput Format: Pytorch Tensor
\nOutput Parameters: Six Dimensional (6D) (batch, time, lead time, variable,\nlatitude, longitude)
\nOther Properties Related to Output:

Output latitude/longitude grid: 0.25 degree 721 x 1440, same as input.
Output state weather variables: same as above.

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

Software Integration

Runtime Engine: Pytorch
\nSupported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Hopper
NVIDIA Turing

Supported Operating System:

Linux

Model Version:

Model Version: v1

Training, Testing, and Evaluation Datasets:

Total size (in number of data points): 110,960
\nTotal number of datasets: 1
\nDataset partition: training 95%, testing 2.5%, validation 2.5%

Training Dataset:

Link: ERA5

Data Collection Method by dataset

Automatic/Sensors

Labeling Method by dataset

Automatic/Sensors

Properties:\nERA5 data for the period 1980-2015. 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.

Testing Dataset:

Link: ERA5

Data Collection Method by dataset

Automatic/Sensors

Labeling Method by dataset

Automatic/Sensors

Properties:\nERA5 data for the period 2016-2017. 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:\nERA5 data for the period 2018-2019. 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.

Inference:

Acceleration Engine: Pytorch
\nTest Hardware:

A100
H100
L40S

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\nwork with their internal model team to ensure this model meets requirements for the\nrelevant industry and use case and addresses unforeseen product misuse.

For more detailed information on ethical considerations for this model, please see the\nModel Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.

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

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The approach is\ndesigned to respect spherical geometry and to accurately model the spatially\ncorrelated probabilistic nature of the problem, resulting in stable spectra and\nrealistic dynamics across multiple scales. FourCastNet 3 delivers forecasting accuracy\nthat surpasses leading conventional ensemble models and rivals the best diffusion-based\nmethods, while producing forecasts 8 to 60 times faster than these approaches. In\ncontrast to other ML approaches, FourCastNet 3 demonstrates excellent probabilistic\ncalibration and retains realistic spectra, even at extended lead times of up to 60 days.\nAll of these advances are realized using a purely convolutional neural network\narchitecture specifically tailored for spherical geometry. Scalable and eﬃcient\nlarge-scale training on 1024 GPUs and more is enabled by a novel training paradigm for\ncombined model- and data-parallelism, inspired by domain decomposition methods in\nclassical numerical models. Additionally, FourCastNet 3 enables rapid inference on a\nsingle GPU, producing a 60-day global forecast at 0.25°, 6-hourly resolution in under\n4 minutes. Its computational efficiency, medium-range probabilistic skill, spectral\nfidelity, and rollout stability at subseasonal timescales make it a strong candidate\nfor improving meteorological forecasting and early warning systems through large\nensemble predictions.\n\n![FCN3 15 member ensemble](https://raw.githubusercontent.com/NVIDIA/makani/main/images/fcn3_ens15.gif)\n\nThis model is ready for commercial/non-commercial use.\n\n### License/Terms of Use:\n\n[Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0)\n\n### Deployment Geography:\n\nGlobal\n\n### Use Case:\n\nIndustry, academic, and government research teams interested in medium-range and\nsubseasonal-to-seasonal weather forecasting, and climate modeling.\n\n### Release Date:\n\nNGC 07/18/2025\n\n## Reference:\n\n**Papers**:\n\n- [FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale](https://arxiv.org/abs/2507.12144v2)\n- [Neural Operators with Localized Integral and Differential Kernels](https://arxiv.org/abs/2402.16845)\n- [Huge Ensembles Part I: Design of Ensemble Weather Forecasts using Spherical Fourier Neural Operators](https://arxiv.org/abs/2408.03100)\n- [Huge Ensembles Part II: Properties of a Huge Ensemble of Hindcasts Generated with Spherical Fourier Neural Operators](https://arxiv.org/abs/2408.01581)\n- [Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere](https://arxiv.org/abs/2306.03838)\n\n**Code**:\n\n- [Makani](https://github.com/NVIDIA/makani)\n\n- [PhysicsNeMo](https://github.com/NVIDIA/physicsnemo)\n\n- [Earth2Studio](https://github.com/NVIDIA/earth2studio)\n\n- [torch-harmonics](https://github.com/NVIDIA/torch-harmonics)\n\n## Model Architecture:\n\n**Architecture Type:** Spherical Neural Operator. A fully convolutional architecture\nbased on group convolutions defined on the sphere. Leverages both local and global\nconvolutions. For details regarding the architecture refer to the\n[FourCastNet 3 paper](https://arxiv.org/abs/2507.12144v1).
\n\n**Network Architecture:** N/A
\n\n**Number of model parameters:** 710,867,670\n\n**Model datatype:** We recommend that the model is run in AMP with bf16, however, the\ninputs and outputs are typically float32.\n\n## Input:\n\n**Input Type:**\n\n- Tensor (72 surface and pressure-level variables)\n\n**Input Format:** PyTorch Tensor
\n**Input Parameters:**\n\n- Six Dimensional (6D) (batch, time, lead time, variable, latitude, longitude)
\n\n**Other Properties Related to Input:**\n\n- Input equi-rectangular latitude/longitude grid: 0.25 degree 721 x 1440\n- Input state weather variables: `u10m`, `v10m`, `u100m`, `v100m`, `t2m`, `msl`,\n`tcwv`, `u50`, `u100`, `u150`, `u200`, `u250`, `u300`, `u400`, `u500`, `u600`, `u700`,\n`u850`, `u925`, `u1000`, `v50`, `v100`, `v150`, `v200`, `v250`, `v300`, `v400`, `v500`,\n`v600`, `v700`, `v850`, `v925`, `v1000`, `z50`, `z100`, `z150`, `z200`, `z250`, `z300`,\n`z400`, `z500`, `z600`, `z700`, `z850`, `z925`, `z1000`, `t50`, `t100`, `t150`, `t200`,\n`t250`, `t300`, `t400`, `t500`, `t600`, `t700`, `t850`, `t925`, `t1000`, `q50`, `q100`,\n`q150`, `q200`, `q250`, `q300`, `q400`, `q500`, `q600`, `q700`, `q850`, `q925`, `q1000`\n- Time: datetime64\n\nFor variable name information, review the Lexicon at [Earth2Studio](https://github.com/NVIDIA/earth2studio).\n\n## Output:\n\n**Output Type:** Tensor (72 surface and pressure-level variables)
\n**Output Format:** Pytorch Tensor
\n**Output Parameters:** Six Dimensional (6D) (batch, time, lead time, variable,\n latitude, longitude)
\n**Other Properties Related to Output:**\n\n- Output latitude/longitude grid: 0.25 degree 721 x 1440, same as input.\n- Output state weather variables: same as above.\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems.\nBy leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA\nlibraries), the model achieves faster training and inference times compared to\nCPU-only solutions.\n\n## Software Integration\n\n**Runtime Engine:** Pytorch
\n**Supported Hardware Microarchitecture Compatibility:**
\n\n- NVIDIA Ampere
\n- NVIDIA Hopper
\n- NVIDIA Turing
\n\n**Supported Operating System:**\n\n- Linux
\n\n## Model Version:\n\n**Model Version:** v1
\n\n## Training, Testing, and Evaluation Datasets:\n\n**Total size (in number of data points):** 110,960
\n**Total number of datasets:** 1
\n**Dataset partition:** training 95%, testing 2.5%, validation 2.5%
\n\n## Training Dataset:\n\n**Link:** [ERA5](https://cds.climate.copernicus.eu/)
\n\n**Data Collection Method by dataset**
\n\n- Automatic/Sensors
\n\n**Labeling Method by dataset**
\n\n- Automatic/Sensors
\n\n**Properties:**\nERA5 data for the period 1980-2015. 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## Testing Dataset:\n\n**Link:** [ERA5](https://cds.climate.copernicus.eu/)
\n\n**Data Collection Method by dataset**
\n\n- Automatic/Sensors
\n\n**Labeling Method by dataset**
\n\n- Automatic/Sensors
\n\n**Properties:**\nERA5 data for the period 2016-2017. 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\n- Automatic/Sensors
\n\n**Labeling Method by dataset**
\n\n- Automatic/Sensors
\n\n**Properties:**\nERA5 data for the period 2018-2019. 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## Inference:\n\n**Acceleration Engine:** Pytorch
\n**Test Hardware:**\n\n- A100
\n- H100
\n- L40S
\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\nwork with their internal model team to ensure this model meets requirements for the\nrelevant industry and use case and addresses unforeseen product misuse.\n\nFor more detailed information on ethical considerations for this model, please see the\nModel Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.\n\nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","Earth-2 FourCastNet 3",["D",1753808924738],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/earth-2/models/fourcastnet3","rawImageUrl","canGuestDownload","latestVersionSizeInBytes","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: | Not Applicable (N/A)\nBias Metric (If Measured) : | N/A\nWhich characteristic (feature) show(s) the greatest difference in performance?: | N/A","explainability","Field | Response\n:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nIntended Task/Domain: | Weather Forecasting and Simulation\nModel Type: | Spherical Neural Operator\nIntended Users: | Weather Forecasters, Researchers, Related Industrial users.\nOutput: | Tensor representing 72 atmospheric variables\nDescribe how the model works: | Input variables are fed through a spherical convolutional architecture to produce the weather state in the next 6 hours.\nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable (N/A)\nTechnical Limitations & Mitigation: | The model may perform poorly for systems that are not similar to those in the training data, namely for rare weather phenomena or weather behavior outside of the 1980-2016 training dataset. There is no mechanism to enforce physical consistency for predictions.\nVerified to have met prescribed NVIDIA quality standards: | Yes\nPerformance Metrics: | Accuracy (ACC), Error (RMSE), and Probabilistic Calibration (CRPS)\nPotential Known Risks: | This model may incorrectly predict future weather states and phenomenon\nLicensing: | [Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0).","privacy","Field | Response\n:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------\nGeneratable or reverse engineerable personal data? | No\nPersonal data used to create this model? | No\nHow often is dataset reviewed? | Before Release\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? | No, not possible with externally-sourced data.\nApplicable Privacy Policy: | https://www.nvidia.com/en-us/about-nvidia/privacy-policy/","safetyAndSecurity","Field | Response\n:---------------------------------------------------|:----------------------------------\nModel Application Field(s): | Climate\nDescribe the life critical impact (if present). | FourCastNet 3 achieves state of the art forecasting accuracy and can be used to forecast, among other events, extreme weather. Like any weather model, it is important to understand the accuracy and limitations - please see [FourCastNet 3 paper](https://arxiv.org/abs/2507.12144v1) to learn more.\nUse Case Restrictions: | Abide by [Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0).\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",[],"displayLabels",[244,245,246,247,248,249,250,251,252,253,254,255],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_210":296,"_257":297,"_259":298,"_261":262},{"_210":293,"_257":294,"_259":295,"_261":262},{"_210":290,"_257":291,"_259":292,"_261":262},{"_210":287,"_257":288,"_259":289,"_261":262},{"_210":284,"_257":285,"_259":286,"_261":262},{"_210":281,"_257":282,"_259":283,"_261":262},{"_210":278,"_257":279,"_259":280,"_261":262},{"_210":274,"_257":275,"_259":276,"_261":262},{"_210":272,"_257":272,"_259":273,"_261":262},{"_210":268,"_257":269,"_259":270,"_261":262},{"_210":265,"_257":265,"_259":266,"_261":262},{"_210":256,"_257":258,"_259":260,"_261":262},"Simulation and Modeling","value","uscs_simulation_and_modeling","filter",{"_263":264,"_257":258},"category","label","field","application","PyTorch",{"_263":267,"_257":265},"framework","PhysicsNeMo","pltfm_modulus",{"_263":271,"_257":269},"platform","NSPECT-BSYX-DL9J",{"_263":262,"_257":272},"HPC / Supercomputing","indus_hpc_supercomputing",{"_263":277,"_257":275},"industry","High Performance Computing","uscs_high_performance_computing",{"_263":264,"_257":279},"Forecasting","uscs_forecasting",{"_263":264,"_257":282},"Financial Services","indus_financial_services",{"_263":277,"_257":285},"Energy","indus_energy",{"_263":277,"_257":288},"CUDA","pltfm_cuda",{"_263":271,"_257":291},"Agriculture","indus_agriculture",{"_263":277,"_257":294},"Academia / Higher Education","indus_academia_higher_education",{"_263":277,"_257":297},{"_121":1470,"_123":1471},"ngcApps",[1423],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1404":1405,"_1406":1407,"_1408":1409,"_1410":1411,"_1412":310,"_152":334,"_1413":1414,"_1415":59,"_1416":89,"_1417":89,"_1418":1419,"_1420":89,"_1421":1422},"amplitude",{"_1401":1402,"_1006":-7,"_1403":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1395":1396,"_1397":1398,"_1399":1400},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1281":181,"_1282":1283,"_1284":1285,"_1286":1287},"announcements",[],"consentState",{"_372":1271,"_1272":89,"_1273":34,"_1274":34,"_1275":1276},"consentConfig",{"_354":355,"_356":357,"_358":359,"_360":361,"_362":363,"_364":365,"_366":367,"_368":369,"_370":371,"_372":373},"showContactAdmin",1784055169154,[],[],[348,349,350,351,352,353],{"_48":63,"_50":74,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":68,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":53,"_54":55,"_56":57,"_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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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.",[522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556],[395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411],{"_412":518,"_210":518,"_180":181,"_386":519},{"_412":513,"_210":513,"_180":181,"_386":514},{"_412":509,"_210":509,"_180":181,"_386":510},{"_412":504,"_210":504,"_180":181,"_386":505},{"_412":499,"_210":499,"_180":181,"_386":500},{"_412":487,"_210":487,"_180":181,"_386":488},{"_412":475,"_210":475,"_180":181,"_386":476},{"_412":470,"_210":470,"_180":181,"_386":471},{"_412":459,"_210":459,"_180":181,"_386":460},{"_412":454,"_210":454,"_180":181,"_386":455},{"_412":449,"_210":449,"_180":181,"_386":450},{"_412":444,"_210":444,"_180":181,"_386":445},{"_412":439,"_210":439,"_180":181,"_386":440},{"_412":434,"_210":434,"_180":181,"_386":435},{"_412":429,"_210":429,"_180":181,"_386":430},{"_412":422,"_210":422,"_180":181,"_386":423},{"_412":413,"_210":413,"_180":181,"_386":414},"hostName","amazon-adsystem.com",[415,416],{"_78":421,"_418":413,"_419":34,"_180":420},{"_78":417,"_418":413,"_419":34,"_180":420},"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",[424,425],{"_78":428,"_418":422,"_419":34,"_180":427},{"_78":426,"_418":422,"_419":34,"_180":427},"IDE","This domain is owned by Doubleclick (Google). 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It is used as a URL shortening service, which allows Twitter to collect data on how links are shared.","snowplow.com",[451],{"_78":452,"_418":449,"_419":34,"_180":453},"sp","Testing","google.com",[456],{"_78":457,"_418":454,"_419":34,"_180":458},"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",[461,462,463,464],{"_78":469,"_418":459,"_419":34,"_180":466},{"_78":468,"_418":459,"_419":34,"_180":466},{"_78":467,"_418":459,"_419":34,"_180":466},{"_78":465,"_418":459,"_419":34,"_180":466},"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",[472],{"_78":473,"_418":470,"_419":34,"_180":474},"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",[477,478,479,480],{"_78":486,"_418":475,"_419":34,"_180":482},{"_78":484,"_418":475,"_419":34,"_180":485},{"_78":483,"_418":475,"_419":34,"_180":482},{"_78":481,"_418":475,"_419":34,"_180":482},"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",[489,490,491,492],{"_78":498,"_418":487,"_419":34,"_180":496},{"_78":497,"_418":487,"_419":34,"_180":496},{"_78":495,"_418":487,"_419":34,"_180":496},{"_78":493,"_418":487,"_419":34,"_180":494},"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",[501],{"_78":502,"_418":499,"_419":34,"_180":503},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[506],{"_78":507,"_418":504,"_419":34,"_180":508},"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",[511],{"_78":512,"_418":509,"_419":34,"_180":453},"R","6sc.co",[515],{"_78":516,"_418":513,"_419":34,"_180":517},"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.",[644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664],[609,610,611,612,613],{"_412":504,"_210":504,"_180":181,"_386":635},{"_412":470,"_210":470,"_180":181,"_386":630},{"_412":623,"_210":623,"_180":181,"_386":624},{"_412":619,"_210":619,"_180":181,"_386":620},{"_412":614,"_210":614,"_180":181,"_386":615},"nvcrm.my.site.com",[616],{"_78":617,"_418":614,"_419":34,"_180":618},"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",[621],{"_78":622,"_418":619,"_419":34,"_180":453},"edge","github.com",[625,626],{"_78":629,"_418":623,"_419":34,"_180":628},{"_78":627,"_418":623,"_419":34,"_180":628},"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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