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GOVERNING DOWNLOAD TERMS: Use of the model is governed by the NVIDIA Open Model Agreement. Additional Information:Apache 2.0,Gemma Terms of UseandGemma Prohibited Use Policy.

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Gemma-4-26B-A4B-IT

Description

gemma-4-26B-A4B-it is a Google-developed multimodal instruction-tuned Mixture-of-Experts model for text, image, and video understanding. The model supports long-context reasoning up to 256K tokens, coding, tool calling, multilingual conversation, multi-turn conversation, visual reasoning, and interleaved multimodal input. The 26B A4B configuration has approximately 25.2B total parameters and approximately 3.8B active parameters per token.

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

Third-Party Community Consideration:

This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA gemma-4-26B-A4B-it Model Card.

License and Terms of Use:

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

Additional Information: Apache 2.0, Gemma Terms of Use, and Gemma Prohibited Use Policy.

Deployment Geography:

Global

Use Case:

Use Case: Developers and enterprises can use this model to build multimodal assistants, coding copilots, document and image understanding workflows, video understanding workflows, long-context analysis systems, tool-calling agents, and multilingual conversational applications.

Release Date:

NGC 06/01/2026 via NGC

HuggingFace: 04/30/2026 via nvidia/Gemma-4-26B-A4B-NVFP4

Reference(s):

References:

Model Architecture:

Architecture Type: Transformer\nNetwork Architecture: Multimodal Mixture-of-Experts conditional generation architecture using Gemma4ForConditionalGeneration, 30 layers, 8 active experts per token, 128 total experts, one shared expert, sliding-window attention, and a vision encoder with approximately 550M parameters.\nTotal Parameters: 25.2B\nActive Parameters: 3.8B\nVocabulary Size: 262K tokens\nBase Model: google/gemma-4-26B-A4B

Input:

Input Types: Text, Image, Video\nInput Formats: Text: String; Image: Red, Green, Blue (RGB); Video: mp4, mov, webm\nInput Parameters: One Dimensional (1D), Two Dimensional (2D), Three Dimensional (3D)\nOther Input Properties: Text prompts, chat messages, code, tool-use instructions, image inputs, interleaved image-and-text inputs, and video inputs. The model supports context length up to 256K tokens.\nInput Context Length (ISL): 256K tokens

Output:

Output Types: Text\nOutput Format: String\nOutput Parameters: One Dimensional (1D)\nOther Output Properties: Generated natural language, code, structured text, tool-call arguments, reasoning responses, image-grounded answers, and video-grounded answers.

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:

Runtime Engines:\nvLLM

Supported Hardware:

NVIDIA B200 Tensor Core GPU
NVIDIA H20 Tensor Core GPU
NVIDIA H200 Tensor Core GPU
NVIDIA L40S GPU
NVIDIA DGX Spark

Operating Systems: Linux

The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.

Model Version(s)

nvidia/Gemma-4-26B-A4B-NVFP4

Training, Testing, and Evaluation Datasets

Training Dataset

Data Modality: Text, image, video, and code\nText Training Data Size: Undisclosed\nImage Training Data Size: Undisclosed\nVideo Training Data Size: Undisclosed\nData Collection Method by dataset: Undisclosed\nLabeling Method by dataset: Undisclosed\nProperties: The training corpus includes large-scale multimodal data spanning web documents, code, mathematics, and images, with multilingual coverage and a January 2025 cutoff.

Testing Dataset

Data Collection Method by dataset: Undisclosed\nLabeling Method by dataset: Undisclosed\nProperties: Undisclosed

Evaluation Dataset

Data Collection Method by dataset: [Hybrid: Automated, Human]\nLabeling Method by dataset: [Hybrid: Automated, Human]\nProperties: The evaluation benchmarks included text, coding, reasoning, long-context, and vision benchmarks including MMLU Pro, AIME 2026, LiveCodeBench v6, Codeforces ELO, GPQA Diamond, Tau2, HLE, BigBench Extra Hard, MMMLU, MMMU Pro, OmniDocBench, MATH-Vision, MedXPertQA MM, and MRCR v2.

Inference

Engine: vLLM\nTest Hardware:

NVIDIA B200 Tensor Core GPU
NVIDIA H20 Tensor Core GPU
NVIDIA H200 Tensor Core GPU
NVIDIA L40S GPU

Ethical Considerations

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

Please make sure you have proper rights and permissions for all input image content; if image includes people, personal health information, or intellectual property, the image generated will not blur or maintain proportions of image subjects included.

Users are responsible for model inputs and outputs. Users are responsible for ensuring safe integration of this model, including implementing guardrails as well as other safety mechanisms, prior to deployment.

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

Get Help

Getting started with the NIM

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

NVIDIA Developer Community Forum

Get access to community knowledge base articles and support cases (https://forums.developer.nvidia.com/)

","resourceVersions","resourceVersionsTotal","hasMoreVersions","versionMetadata","versionNotFound","isLoggedIn","latestVersionPath","/orgs/nim/google/models/gemma-4-26b-a4b-it/-","version","nim-bf16","imageReference","cliDownloadCommand","ngc registry model download-version nim/google/gemma-4-26b-a4b-it:nim-bf16","helmFetchCommand","viewAllVersionsPath","/orgs/nim/google/models/gemma-4-26b-a4b-it/nim-bf16/version-history","accessCheckResult",{"_109":110,"_111":16},"hasRelatedCollections","context",{"_105":106,"_107":108},1783988000367,[],[],[95,96,97,98,99,100],{"_36":51,"_38":56,"_40":57,"_42":58,"_44":59,"_46":47},{"_36":51,"_38":55,"_40":41,"_42":102,"_44":104,"_46":47},{"_36":51,"_38":54,"_40":41,"_42":102,"_44":103,"_46":47},{"_36":51,"_38":52,"_40":57,"_42":58,"_44":101,"_46":47},{"_36":48,"_38":49,"_40":41,"_42":43,"_44":50,"_46":47},{"_36":37,"_38":39,"_40":41,"_42":43,"_44":45,"_46":47},55,403,57,63,"orgName","nim","teamName","google","reason","login_required","allowed","id","nim/google/gemma-4-26b-a4b-it","key","nim/google/models/gemma-4-26b-a4b-it","resourceType","models","resourceId","gemma-4-26b-a4b-it","shortDescription","Google Gemma 4 26B A4B IT is a multimodal instruction-tuned Mixture-of-Experts model packaged for NVIDIA NIM.","description","# Gemma-4-26B-A4B-IT\n\n## Description\n**gemma-4-26B-A4B-it** is a Google-developed multimodal instruction-tuned Mixture-of-Experts model for text, image, and video understanding. The model supports long-context reasoning up to 256K tokens, coding, tool calling, multilingual conversation, multi-turn conversation, visual reasoning, and interleaved multimodal input. The 26B A4B configuration has approximately 25.2B total parameters and approximately 3.8B active parameters per token.\n\n*This model is ready for commercial/non-commercial use.*\n\n## Third-Party Community Consideration:\nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA [gemma-4-26B-A4B-it Model Card](https://huggingface.co/google/gemma-4-26B-A4B-it/).\n\n## License and Terms of Use:\n**GOVERNING TERMS:** The NIM container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and the [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/). Use of the model is governed by the [NVIDIA Open Model Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-agreement/).\n\nAdditional Information: [Apache 2.0](https://ai.google.dev/gemma/apache_2), [Gemma Terms of Use](https://ai.google.dev/gemma/terms), and [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy).\n\n## Deployment Geography:\nGlobal\n\n## Use Case:\n**Use Case:** Developers and enterprises can use this model to build multimodal assistants, coding copilots, document and image understanding workflows, video understanding workflows, long-context analysis systems, tool-calling agents, and multilingual conversational applications.\n\n## Release Date:\n**NGC** 06/01/2026 via **NGC**\n\n**HuggingFace:** 04/30/2026 via [nvidia/Gemma-4-26B-A4B-NVFP4](https://huggingface.co/nvidia/Gemma-4-26B-A4B-NVFP4)\n\n## Reference(s):\n**References:**\n- [Google gemma-4-26B-A4B-it Model Page](https://huggingface.co/google/gemma-4-26B-A4B-it/)\n- [NVIDIA Gemma-4-26B-A4B-NVFP4 Model Page](https://huggingface.co/nvidia/Gemma-4-26B-A4B-NVFP4)\n- [Google Gemma documentation](https://ai.google.dev/gemma)\n\n## Model Architecture:\n**Architecture Type:** Transformer\n**Network Architecture:** Multimodal Mixture-of-Experts conditional generation architecture using `Gemma4ForConditionalGeneration`, 30 layers, 8 active experts per token, 128 total experts, one shared expert, sliding-window attention, and a vision encoder with approximately 550M parameters.\n**Total Parameters:** 25.2B\n**Active Parameters:** 3.8B\n**Vocabulary Size:** 262K tokens\n**Base Model:** google/gemma-4-26B-A4B\n\n### Input:\n**Input Types:** Text, Image, Video\n**Input Formats:** Text: String; Image: Red, Green, Blue (RGB); Video: mp4, mov, webm\n**Input Parameters:** One Dimensional (1D), Two Dimensional (2D), Three Dimensional (3D)\n**Other Input Properties:** Text prompts, chat messages, code, tool-use instructions, image inputs, interleaved image-and-text inputs, and video inputs. The model supports context length up to 256K tokens.\n**Input Context Length (ISL):** 256K tokens\n\n### Output:\n**Output Types:** Text\n**Output Format:** String\n**Output Parameters:** One Dimensional (1D)\n**Other Output Properties:** Generated natural language, code, structured text, tool-call arguments, reasoning responses, image-grounded answers, and video-grounded answers.\n\n__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.__\n\n## Software Integration:\n**Runtime Engines:**\nvLLM\n\n**Supported Hardware:**\n- NVIDIA B200 Tensor Core GPU\n- NVIDIA H20 Tensor Core GPU\n- NVIDIA H200 Tensor Core GPU\n- NVIDIA L40S GPU\n- NVIDIA DGX Spark\n\n**Operating Systems:** Linux\n\n__The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.__\n\n## Model Version(s)\nnvidia/Gemma-4-26B-A4B-NVFP4\n\n## Training, Testing, and Evaluation Datasets\n\n### Training Dataset\n**Data Modality:** Text, image, video, and code\n**Text Training Data Size:** Undisclosed\n**Image Training Data Size:** Undisclosed\n**Video Training Data Size:** Undisclosed\n**Data Collection Method by dataset:** Undisclosed\n**Labeling Method by dataset:** Undisclosed\n**Properties:** The training corpus includes large-scale multimodal data spanning web documents, code, mathematics, and images, with multilingual coverage and a January 2025 cutoff.\n\n### Testing Dataset\n**Data Collection Method by dataset:** Undisclosed\n**Labeling Method by dataset:** Undisclosed\n**Properties:** Undisclosed\n\n### Evaluation Dataset\n**Data Collection Method by dataset:** [Hybrid: Automated, Human]\n**Labeling Method by dataset:** [Hybrid: Automated, Human]\n**Properties:** The evaluation benchmarks included text, coding, reasoning, long-context, and vision benchmarks including MMLU Pro, AIME 2026, LiveCodeBench v6, Codeforces ELO, GPQA Diamond, Tau2, HLE, BigBench Extra Hard, MMMLU, MMMU Pro, OmniDocBench, MATH-Vision, MedXPertQA MM, and MRCR v2.\n\n## Inference\n**Engine:** vLLM\n**Test Hardware:**\n- NVIDIA B200 Tensor Core GPU\n- NVIDIA H20 Tensor Core GPU\n- NVIDIA H200 Tensor Core GPU\n- NVIDIA L40S GPU\n\n## Ethical Considerations\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal developer team to ensure these software components meet requirements for the relevant industry and use case and address unforeseen product misuse.\n\nPlease make sure you have proper rights and permissions for all input image content; if image includes people, personal health information, or intellectual property, the image generated will not blur or maintain proportions of image subjects included.\n\nUsers are responsible for model inputs and outputs. Users are responsible for ensuring safe integration of this model, including implementing guardrails as well as other safety mechanisms, prior to deployment.\n\nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/)\n\n## Get Help\n\n### Getting started with the NIM\nDeploying and integrating the NIM is straightforward thanks to our industry standard APIs. Visit the [NIM Model page](https://catalog.ngc.nvidia.com/orgs/nim/teams/google/models/gemma-4-26b-a4b-it) for release documentation, deployment guides and more.\n\n### NVIDIA Developer Community Forum\nGet access to community knowledge base articles and support cases (https://forums.developer.nvidia.com/)","displayName","Gemma 4 26B A4B IT","updatedDate",["D",1780359433941],"publisher","Google","latestVersion","versions","collectionCounts","imageUrl","/api/image/nim/google/models/gemma-4-26b-a4b-it","rawImageUrl","","canGuestDownload","latestVersionSizeInBytes",51644335089,"licenseTerms",{"_188":61,"_189":14,"_190":191,"_192":16},"isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[182],"displayLabels",[159,160,161,162,163,164],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_124":180,"_166":180,"_167":181,"_169":170},{"_124":178,"_166":178,"_167":179,"_169":170},{"_124":176,"_166":176,"_167":177,"_169":170},{"_124":174,"_166":174,"_167":175,"_169":170},{"_124":172,"_166":172,"_167":173,"_169":170},{"_124":165,"_166":165,"_167":168,"_169":170},"vLLM","value","filter",{"_171":170,"_166":165},"category","label","field","Vision Language Model",{"_171":170,"_166":172},"NSPECT-LOBX-F26V",{"_171":170,"_166":174},"Multimodal",{"_171":170,"_166":176},"Gemma",{"_171":170,"_166":178},"False",{"_171":170,"_166":180},{"_124":183,"_166":184,"_167":185,"_169":186},"NVIDIA NIM","nimmcro_nvidia_nim",{"_171":187,"_166":184},"feature","nvidia_nim","governingTerms","licenseId","licenseVersion","v1.0","needsAcceptance","appConfig",{"_1375":1376,"_227":1377},"ngcApps",[1328],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1309":1310,"_1311":1312,"_1313":1314,"_1315":1316,"_1317":205,"_78":232,"_1318":1319,"_1320":47,"_1321":65,"_1322":65,"_1323":1324,"_1325":65,"_1326":1327},"amplitude",{"_1306":1307,"_908":-7,"_1308":16},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1300":1301,"_1302":1303,"_1304":1305},"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,"1.7.3","defaultSearchSuggestions",{"_1183":136,"_1184":1185,"_1186":1187,"_1188":1189},"announcements",[],"consentState",{"_271":1173,"_1174":65,"_1175":16,"_1176":16,"_1177":1178},"consentConfig",{"_253":254,"_255":256,"_257":258,"_259":260,"_261":262,"_263":264,"_265":266,"_267":268,"_269":270,"_271":272},"showContactAdmin",1783988000306,[],[],[246,247,248,249,250,251],{"_36":51,"_38":56,"_40":53,"_42":-5,"_44":-5,"_46":47},{"_36":51,"_38":55,"_40":53,"_42":-5,"_44":-5,"_46":47},{"_36":51,"_38":54,"_40":53,"_42":-5,"_44":-5,"_46":47},{"_36":51,"_38":52,"_40":53,"_42":-5,"_44":-5,"_46":47},{"_36":48,"_38":49,"_40":53,"_42":-5,"_44":-5,"_46":47},{"_36":37,"_38":39,"_40":41,"_42":43,"_44":252,"_46":47},4,"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",{"_1165":1166,"_1167":1168,"_1169":1170,"_1171":1172},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[273,274,275,276,277],{"_278":816,"_280":817,"_122":818,"_283":65,"_284":819,"_286":820,"_288":821},{"_278":605,"_280":606,"_122":607,"_283":16,"_284":608,"_286":609,"_288":610},{"_278":505,"_280":506,"_122":507,"_283":16,"_284":252,"_286":508,"_288":509},{"_278":290,"_280":291,"_122":292,"_283":16,"_284":293,"_286":294,"_288":295},{"_278":279,"_280":281,"_122":282,"_283":16,"_284":285,"_286":287,"_288":289},"optanonGroupId","C0005","name","Social Media Cookies","These cookies are set by a range of social media services that we have added to the site to enable you to share our content with your friends and networks. They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457],[296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312],{"_313":419,"_124":419,"_122":136,"_286":420},{"_313":414,"_124":414,"_122":136,"_286":415},{"_313":410,"_124":410,"_122":136,"_286":411},{"_313":405,"_124":405,"_122":136,"_286":406},{"_313":400,"_124":400,"_122":136,"_286":401},{"_313":388,"_124":388,"_122":136,"_286":389},{"_313":376,"_124":376,"_122":136,"_286":377},{"_313":371,"_124":371,"_122":136,"_286":372},{"_313":360,"_124":360,"_122":136,"_286":361},{"_313":355,"_124":355,"_122":136,"_286":356},{"_313":350,"_124":350,"_122":136,"_286":351},{"_313":345,"_124":345,"_122":136,"_286":346},{"_313":340,"_124":340,"_122":136,"_286":341},{"_313":335,"_124":335,"_122":136,"_286":336},{"_313":330,"_124":330,"_122":136,"_286":331},{"_313":323,"_124":323,"_122":136,"_286":324},{"_313":314,"_124":314,"_122":136,"_286":315},"hostName","amazon-adsystem.com",[316,317],{"_280":322,"_319":314,"_320":16,"_122":321},{"_280":318,"_319":314,"_320":16,"_122":321},"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",[325,326],{"_280":329,"_319":323,"_320":16,"_122":328},{"_280":327,"_319":323,"_320":16,"_122":328},"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",[332],{"_280":333,"_319":330,"_320":16,"_122":334},"MUID","This domain is owned by Mircosoft - it is the site for the search engine Bing.","assets.adobedtm.com",[337],{"_280":338,"_319":335,"_320":16,"_122":339},"kndctr_F207D74D549850760A4C98C6_AdobeOrg_consent","If you know anything about this host and its uses please add your contributions here. ","tiktok.com",[342],{"_280":343,"_319":340,"_320":16,"_122":344},"_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",[347],{"_280":348,"_319":345,"_320":16,"_122":349},"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",[352],{"_280":353,"_319":350,"_320":16,"_122":354},"sp","Testing","google.com",[357],{"_280":358,"_319":355,"_320":16,"_122":359},"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",[362,363,364,365],{"_280":370,"_319":360,"_320":16,"_122":367},{"_280":369,"_319":360,"_320":16,"_122":367},{"_280":368,"_319":360,"_320":16,"_122":367},{"_280":366,"_319":360,"_320":16,"_122":367},"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",[373],{"_280":374,"_319":371,"_320":16,"_122":375},"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",[378,379,380,381],{"_280":387,"_319":376,"_320":16,"_122":383},{"_280":385,"_319":376,"_320":16,"_122":386},{"_280":384,"_319":376,"_320":16,"_122":383},{"_280":382,"_319":376,"_320":16,"_122":383},"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",[390,391,392,393],{"_280":399,"_319":388,"_320":16,"_122":397},{"_280":398,"_319":388,"_320":16,"_122":397},{"_280":396,"_319":388,"_320":16,"_122":397},{"_280":394,"_319":388,"_320":16,"_122":395},"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",[402],{"_280":403,"_319":400,"_320":16,"_122":404},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[407],{"_280":408,"_319":405,"_320":16,"_122":409},"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",[412],{"_280":413,"_319":410,"_320":16,"_122":354},"R","6sc.co",[416],{"_280":417,"_319":414,"_320":16,"_122":418},"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.",[545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565],[510,511,512,513,514],{"_313":405,"_124":405,"_122":136,"_286":536},{"_313":371,"_124":371,"_122":136,"_286":531},{"_313":524,"_124":524,"_122":136,"_286":525},{"_313":520,"_124":520,"_122":136,"_286":521},{"_313":515,"_124":515,"_122":136,"_286":516},"nvcrm.my.site.com",[517],{"_280":518,"_319":515,"_320":16,"_122":519},"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",[522],{"_280":523,"_319":520,"_320":16,"_122":354},"edge","github.com",[526,527],{"_280":530,"_319":524,"_320":16,"_122":529},{"_280":528,"_319":524,"_320":16,"_122":529},"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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