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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 Use and Gemma 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: Use of the model is governed by the NVIDIA Open Model Agreement.\nAdditional 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/)

Enterprise Support

Get access to knowledge base articles and support cases or submit a ticket.

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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:** 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/).\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/)
\n## Enterprise Support\nGet access to knowledge base articles and support cases or [submit a ticket](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).","displayName","Gemma 4 26B A4B IT",["D",1787959712053],"publisher","Google","latestVersion","versions","collectionCounts","imageUrl","/api/image/nim/google/models/gemma-4-26b-a4b-it","rawImageUrl","canGuestDownload","latestVersionSizeInBytes",51644339073,"licenseTerms",{"_272":273,"_274":16,"_275":276,"_277":20,"_278":279},"isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[263,264],"displayLabels",[251,252,253],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_218":261,"_255":261,"_256":262,"_72":78},{"_218":259,"_255":259,"_256":260,"_72":78},{"_218":254,"_255":254,"_256":257,"_72":78},"NSPECT-LOBX-F26V","value","filter",{"_258":78,"_255":254},"field","Multimodal",{"_258":78,"_255":259},"Gemma",{"_258":78,"_255":261},{"_218":270,"_255":270,"_256":271,"_72":268},{"_218":265,"_255":266,"_256":267,"_72":268},"NVIDIA NIM","nimmcro_nvidia_nim",{"_258":269,"_255":266},"feature","nvidia_nim","NVIDIA AI Enterprise Supported",{"_258":78,"_255":270},"governingTerms","

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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For more information about our privacy practices, please see our Privacy Policy.","mainText","Cookie Settings","alertNoticeText",{"_1060":1061,"_1062":1063,"_1064":1065,"_1066":1067},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[366,367,368,369,370],{"_371":749,"_198":750,"_163":751,"_375":18,"_376":752,"_378":753,"_380":754},{"_371":652,"_198":653,"_163":654,"_375":20,"_376":53,"_378":655,"_380":656},{"_371":560,"_198":561,"_163":562,"_375":20,"_376":345,"_378":563,"_380":564},{"_371":382,"_198":383,"_163":384,"_375":20,"_376":124,"_378":385,"_380":386},{"_371":372,"_198":373,"_163":374,"_375":20,"_376":377,"_378":379,"_380":381},"optanonGroupId","C0005","Social Media Cookies","These cookies are set by a range of social media services that we have added to the site to enable you to share our content with your friends and networks. They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",[502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523],[387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403],{"_404":497,"_218":497,"_163":164,"_378":498},{"_404":493,"_218":493,"_163":164,"_378":494},{"_404":488,"_218":488,"_163":164,"_378":489},{"_404":484,"_218":484,"_163":164,"_378":485},{"_404":479,"_218":479,"_163":164,"_378":480},{"_404":474,"_218":474,"_163":164,"_378":475},{"_404":466,"_218":466,"_163":164,"_378":467},{"_404":458,"_218":458,"_163":164,"_378":459},{"_404":453,"_218":453,"_163":164,"_378":454},{"_404":448,"_218":448,"_163":164,"_378":449},{"_404":443,"_218":443,"_163":164,"_378":444},{"_404":438,"_218":438,"_163":164,"_378":439},{"_404":433,"_218":433,"_163":164,"_378":434},{"_404":426,"_218":426,"_163":164,"_378":427},{"_404":421,"_218":421,"_163":164,"_378":422},{"_404":414,"_218":414,"_163":164,"_378":415},{"_404":405,"_218":405,"_163":164,"_378":406},"hostName","amazon-adsystem.com",[407,408],{"_198":413,"_410":405,"_411":20,"_163":412},{"_198":409,"_410":405,"_411":20,"_163":412},"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",[416,417],{"_198":420,"_410":414,"_411":20,"_163":419},{"_198":418,"_410":414,"_411":20,"_163":419},"IDE","This domain is owned by Doubleclick (Google). 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It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","linkedin.com",[455],{"_198":456,"_410":453,"_411":20,"_163":457},"bcookie","This domain is owned by LinkedIn, the business networking platform. It typically acts as a third party host where website owners have placed one of its content sharing buttons in their pages, although its content and services can be embedded in other ways. Although such buttons add functionality to the website they are on, cookies are set regardless of whether or not the visitor has an active Linkedin profile, or agreed to their terms and conditions. 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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618],[565,566,567,568,569],{"_404":479,"_218":479,"_163":164,"_378":589},{"_404":453,"_218":453,"_163":164,"_378":584},{"_404":579,"_218":579,"_163":164,"_378":580},{"_404":574,"_218":574,"_163":164,"_378":575},{"_404":570,"_218":570,"_163":164,"_378":571},"nvidia.partners",[572],{"_198":573,"_410":570,"_411":20,"_163":447},"edge","app.hushly.com",[576],{"_198":577,"_410":574,"_411":20,"_163":578},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[581],{"_198":582,"_410":579,"_411":20,"_163":583},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705],[657,658,659,660],{"_404":672,"_218":672,"_163":164,"_378":673},{"_404":667,"_218":667,"_163":164,"_378":668},{"_404":466,"_218":466,"_163":164,"_378":664},{"_404":579,"_218":579,"_163":164,"_378":661},[662],{"_198":663,"_410":579,"_411":18,"_163":583},"_gh_sess",[665],{"_198":666,"_410":466,"_411":18,"_163":473},"YSC","bizible.com",[669],{"_198":670,"_410":667,"_411":20,"_163":671},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[674],{"_198":675,"_410":672,"_411":20,"_163":676},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_198":747,"_410":525,"_411":20,"_163":748},{"_198":732,"_410":746,"_411":18,"_163":733},{"_198":715,"_410":528,"_411":20,"_163":640},{"_198":715,"_410":628,"_411":20,"_163":640},{"_198":744,"_410":525,"_411":20,"_163":745},{"_198":732,"_410":531,"_411":18,"_163":733},{"_198":742,"_410":525,"_411":20,"_163":743},{"_198":732,"_410":528,"_411":18,"_163":733},{"_198":740,"_410":627,"_411":20,"_163":741},{"_198":732,"_410":649,"_411":18,"_163":733},{"_198":712,"_410":528,"_411":20,"_163":714},{"_198":738,"_410":525,"_411":18,"_163":739},{"_198":736,"_410":525,"_411":20,"_163":737},{"_198":734,"_410":525,"_411":18,"_163":735},{"_198":732,"_410":623,"_411":18,"_163":733},{"_198":730,"_410":525,"_411":20,"_163":731},{"_198":728,"_410":525,"_411":20,"_163":729},{"_198":726,"_410":525,"_411":18,"_163":727},{"_198":724,"_410":632,"_411":20,"_163":725},{"_198":712,"_410":623,"_411":20,"_163":714},{"_198":722,"_410":525,"_411":20,"_163":723},{"_198":720,"_410":525,"_411":18,"_163":721},{"_198":718,"_410":525,"_411":20,"_163":719},{"_198":716,"_410":525,"_411":20,"_163":717},{"_198":715,"_410":525,"_411":20,"_163":640},{"_198":712,"_410":713,"_411":20,"_163":714},{"_198":710,"_410":525,"_411":20,"_163":711},{"_198":708,"_410":525,"_411":18,"_163":709},{"_198":706,"_410":525,"_411":20,"_163":707},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. It contains the current session ID, whether the session is excluded due to sampling, and the expiration date of the session. The cookie is extended for an extra 15 minutes every time the user interacts with the website, up to the maximum user session duration (4 hours).","nvweb_A","_ga","This cookie name is associated with Google Universal Analytics - which is a significant update to Google's more commonly used analytics service. This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","tp","This cookie is associated with Adobe Analytics site tracking. It registers if the website's scroll-depth detection is active. This function remembers how far the user has scrolled on the current session across the website's sub-pages.","_hly_vid","This cookie _hly_vid is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.","_session_id","This cookie creates an interim session ID used as an in-session user ID.\n","s_cc","This cookie is associated with the Adobe Site Catalyst. It determines whether cookies are enabled in the web browser.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. It is used to persist the Hotjar User ID, unique to that site on the browser. This ensures that behavior in subsequent visits to the same site will be attributed to the same user ID.","_hjHasCachedUserAttributes","This cookie name is associated with Hotjar which is used to know whether the data set in _hjUserAttributes Local Storage item is up to date or not.","s_ppv","This cookie is associated with Adobe Analytics site tracking and is used to measure a user's scroll activity to see how much of a page they view before moving to another page.","_gid","This cookie name is associated with Google Universal Analytics. This appears to be a new cookie and as of Spring 2017 no information is available from Google. It appears to store and update a unique value for each page visited.","_hjTLDTest","When the Hotjar script executes we try to determine the most generic cookie path we should use, instead of the page hostname. 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