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Field	Response
Participation considerations from adversely impacted groups protected classes in model design and testing:	None
Measures taken to mitigate against unwanted bias:	None

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Nemotron-3-8B-Base-4k

Model Overview

License

The use of this model is governed by the NVIDIA AI Foundation Models Community License Agreement.

Description

Nemotron-3-8B-Base-4k is a large language foundation model for enterprises to build custom LLMs. This foundation model has 8 billion parameters, and supports a context length of 4,096 tokens. Nemotron-3-8B-Base-4k is part of Nemotron-3, which is a family of enterprise ready GPT-based decoder-only generative text models compatible with NVIDIA NeMo Framework. For other models in this collection, see the models page.

NVIDIA NeMo is an end-to-end, cloud-native platform to build, customize, and deploy generative AI models anywhere. It includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models, offering enterprises an easy, cost-effective, and fast way to adopt generative AI.

References

Announcement Blog

Model Architecture

Architecture Type: Transformer

Network Architecture: Generative Pre-Trained Transformer (GPT-3)

Software Integration

\nRuntime Engine(s):\nNVIDIA AI Enterprise

\n\nToolkit:\nNeMo Framework

See NeMo inference container documentation for details on how to setup and deploy an inference server.

Sample Inference Code:

from nemo.deploy import NemoQuery\n\n# In this case, we run inference on the same machine\nnq = NemoQuery(url=\"localhost:8000\", model_name=\"Nemotron-3-8B-4K\")\n\noutput = nq.query_llm(prompts=[\"The meaning of life is\"], max_output_token=200, top_k=1, top_p=0.0, temperature=0.1)\nprint(output)\n

Supported Hardware:\n

H100
A100 80GB, A100 40GB\n

Model Version(s)

\nNemotron-3-8B-base-4k-BF16-1\n

Dataset & Training

\nThe model uses a learning rate of 3e-4 with a warm-up period of 500M tokens and a cosine learning rate annealing schedule for 95% of the total training tokens. The decay stops at a minimum learning rate of 3e-5. The model is trained with a sequence length of 4096 and uses FlashAttention’s Multi-Head Attention implementation. 1,024 A100s were used for 19 days to train the model.\n\nNVIDIA models are trained on a diverse set of public and proprietary datasets. This model was trained on a dataset containing 3.8 Trillion tokens of text. The dataset contains 53 different human languages (including English, German, Russian, Spanish, French, Japanese, Chinese, Italian, and Dutch) and 37 programming languages. The model also uses the training subsets of downstream academic benchmarks from sources like FLANv2, P3, and NaturalInstructions v2. NVIDIA is committed to the responsible development of large language models and conducts reviews of all datasets included in training.\n

Evaluation

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Task	Num-shot	Score
MMLU*	5	54.4
WinoGrande	0	70.9
Hellaswag	0	76.4
ARC Easy	0	72.9
TyDiQA-GoldP**	1	49.2
Lambada	0	70.6
WebQS	0	22.9
PiQA	0	80.4
GSM8K	8-shot w/ maj@8	39.4

`*` The calculation of MMLU follows the original implementation See Hugging Face’s explanation of different implementations of MMLU.

`**` The languages used are Arabic, Bangla, Finnish, Indonesian, Korean, Russian and Swahili.

Intended use

This is a completion model. For best performance, users are encouraged to customize the completion model using NeMo Framework suite of customization tools including Parameter-Efficient Fine-Tuning (P-tuning, Adapters, LoRA), and SFT/RLHF. For chat use cases, please consider using Nemotron-3-8B chat variants.

Ethical use

\nTechnology can have a profound impact on people and the world, and NVIDIA is committed to enabling trust and transparency in AI development. NVIDIA encourages users to adopt principles of AI ethics and trustworthiness to guide your business decisions by following the guidelines in the NVIDIA AI Foundation Models Community License Agreement.\n

Limitations

The model was trained on data that contains toxic language and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts.
The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive.

","resourceVersions",[137,138],"resourceVersionsTotal",1,"hasMoreVersions","versionMetadata",{"_109":110,"_111":112,"_37":113,"_114":-5,"_115":116,"_117":118,"_119":120,"_121":71,"_122":20,"_123":-5,"_124":20,"_125":-5,"_126":20,"_127":-5,"_128":20,"_129":-5,"_130":20,"_131":-5,"_132":20,"_133":-5,"_134":20,"_135":136},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/nemo/models/nemotron-3-8b-base-4k/-","version","-","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/nemo/nemotron-3-8b-base-4k:1.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/nemo/models/nemotron-3-8b-base-4k/-/version-history","accessCheckResult",{"_107":108},"hasRelatedCollections","context",{"_103":104,"_105":106},1784073925891,[],[],[96,97,98,99,100,101],{"_33":48,"_35":57,"_37":50,"_39":51,"_41":58,"_43":44},{"_33":48,"_35":55,"_37":50,"_39":51,"_41":56,"_43":44},{"_33":48,"_35":53,"_37":50,"_39":51,"_41":102,"_43":44},{"_33":48,"_35":49,"_37":50,"_39":51,"_41":52,"_43":44},{"_33":45,"_35":46,"_37":38,"_39":40,"_41":47,"_43":44},{"_33":34,"_35":36,"_37":38,"_39":40,"_41":42,"_43":44},69,"orgName","nvidia","teamName","nemo","allowed",true,"versionId","1.0","description","initial version","UPLOAD_COMPLETE","releaseType","createdDate",["D",1712248927794],"updatedDate",["D",1740788194953],"totalSizeInBytes",17085777920,"totalFileCount","isSigned","malwareScanStatus","isScanned","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance","policies",[],{"_139":80,"_140":142,"_114":-5,"_122":20,"_124":20,"_135":141,"_126":20},{"_139":110,"_140":110,"_114":-5,"_122":20,"_124":20,"_135":141,"_126":20},"id","name",[],"Latest (1.0)","nvidia/nemo/nemotron-3-8b-base-4k","key","nvidia/nemo/models/nemotron-3-8b-base-4k","resourceType","models","resourceId","nemotron-3-8b-base-4k","shortDescription","Nemotron-3-8B-Base-4k is a large language foundation model for enterprises trained by NVIDIA","## Nemotron-3-8B-Base-4k\n### Model Overview\n#### License\nThe use of this model is governed by the [NVIDIA AI Foundation Models Community License Agreement](https://developer.nvidia.com/downloads/nv-ai-foundation-models-license).\n#### Description\nNemotron-3-8B-Base-4k is a large language foundation model for enterprises to build custom LLMs. This foundation model has 8 billion parameters, and supports a context length of 4,096 tokens. Nemotron-3-8B-Base-4k is part of Nemotron-3, which is a family of enterprise ready GPT-based decoder-only generative text models compatible with NVIDIA [NeMo Framework](https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/). For other models in this collection, see the [models page](https://registry.ngc.nvidia.com/models).\n\nNVIDIA NeMo is an end-to-end, cloud-native platform to build, customize, and deploy generative AI models anywhere. It includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models, offering enterprises an easy, cost-effective, and fast way to adopt generative AI.\n#### References\n\n[Announcement Blog](https://developer.nvidia.com/blog/nvidia-ai-foundation-models-build-custom-enterprise-chatbots-and-co-pilots-with-production-ready-llms/)\n\n#### Model Architecture\n\n**Architecture Type:** Transformer\n\n**Network Architecture:** Generative Pre-Trained Transformer (GPT-3)\n\n#### Software Integration\n\n\n**Runtime Engine(s):**\nNVIDIA AI Enterprise\n\n\n\n\n**Toolkit:**\nNeMo Framework\n\nSee [NeMo inference container documentation](https://registry.ngc.nvidia.com/orgs/ea-bignlp/teams/ga-participants/containers/nemofw-inference) for details on how to setup and deploy an inference server.\n\n**Sample Inference Code:**\n\n```python\nfrom nemo.deploy import NemoQuery\n\n# In this case, we run inference on the same machine\nnq = NemoQuery(url=\"localhost:8000\", model_name=\"Nemotron-3-8B-4K\")\n\noutput = nq.query_llm(prompts=[\"The meaning of life is\"], max_output_token=200, top_k=1, top_p=0.0, temperature=0.1)\nprint(output)\n```\n\n**Supported Hardware:**\n\n- H100\n- A100 80GB, A100 40GB\n \n\n#### Model Version(s)\n\n\n`Nemotron-3-8B-base-4k-BF16-1`\n\n\n### Dataset & Training\n\n\nThe model uses a learning rate of 3e-4 with a warm-up period of 500M tokens and a cosine learning rate annealing schedule for 95% of the total training tokens. The decay stops at a minimum learning rate of 3e-5. The model is trained with a sequence length of 4096 and uses FlashAttention’s Multi-Head Attention implementation. 1,024 A100s were used for 19 days to train the model.\n\nNVIDIA models are trained on a diverse set of public and proprietary datasets. This model was trained on a dataset containing 3.8 Trillion tokens of text. The dataset contains 53 different human languages (including English, German, Russian, Spanish, French, Japanese, Chinese, Italian, and Dutch) and 37 programming languages. The model also uses the training subsets of downstream academic benchmarks from sources like FLANv2, P3, and NaturalInstructions v2. NVIDIA is committed to the responsible development of large language models and conducts reviews of all datasets included in training.\n\n\n### Evaluation\n\n| Task | Num-shot | Score |\n|:-----------------------|:------------------------|:--------------|\n| MMLU* | 5 | 54.4 |\n| WinoGrande | 0 | 70.9 |\n| Hellaswag | 0 | 76.4 |\n| ARC Easy | 0 | 72.9 |\n| TyDiQA-GoldP** | 1 | 49.2 |\n| Lambada | 0 | 70.6 |\n| WebQS | 0 | 22.9 |\n| PiQA | 0 | 80.4 |\n| GSM8K | 8-shot w/ maj@8 | 39.4 |\n\n`*` The calculation of MMLU follows the [original implementation](https://github.com/hendrycks/test/pull/13) See [Hugging Face’s explanation](https://huggingface.co/blog/evaluating-mmlu-leaderboard) of different implementations of MMLU.\n\n`**` The languages used are Arabic, Bangla, Finnish, Indonesian, Korean, Russian and Swahili.\n\n### Intended use\n\nThis is a completion model. For best performance, users are encouraged to customize the completion model using NeMo Framework suite of customization tools including Parameter-Efficient Fine-Tuning (P-tuning, Adapters, LoRA), and SFT/RLHF. For chat use cases, please consider using [Nemotron-3-8B chat variants](https://registry.ngc.nvidia.com/models).\n\n#### Ethical use\n\nTechnology can have a profound impact on people and the world, and NVIDIA is committed to enabling trust and transparency in AI development. NVIDIA encourages users to adopt principles of AI ethics and trustworthiness to guide your business decisions by following the guidelines in the [NVIDIA AI Foundation Models Community License Agreement](https://developer.nvidia.com/downloads/nv-ai-foundation-models-license).\n\n### Limitations\n- The model was trained on data that contains toxic language and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts.\n- The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive.","displayName","Nemotron-3-8B-Base-4k",["D",1731434234875],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/nemotron-3-8b-base-4k","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","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: | None\nMeasures taken to mitigate against unwanted bias: | None","explainability","Field | Response\n:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nIntended Application & Domain: | Large Language Model Development\nModel Type: | Generative Pre-Trained Transformer (GPT)\nIntended User: | enterprise developers building large language models.\nOutput: | Text String(s)\nDescribe how the model works: | The network architecture of this model is Generative Pre-Trained Transformer-3 (GPT3). The decoder in GPT-3 generates text by predicting the next word or token based on the context provided in the input sequence using multiple self-attention layers. \nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable\nPerformance Metrics: | Throughput and Latency\nPotential Known Risks: | The Model may produce output that is biased and toxic based on how it is prompted, producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. The model may also amplify biases and return toxic responses especially when prompted with toxic prompts. \nLicensing: | [NVIDIA AI Foundation Models Community License Agreement](https://developer.nvidia.com/downloads/nv-ai-foundation-models-license)","privacy","Field | Response\n:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------\nGeneratable or reverse engineerable personally-identifiable information (PII)? | Neither\nWas consent obtained for any PII used? | Not Applicable\nPII used to create this model? | None Known\nHow often is dataset reviewed? | Before Every Release\nIs a mechanism in place to honor data subject right of access or deletion of personal data? | Not Applicable\nIf PII collected for the development of the model, was it collected directly by NVIDIA? | Not Applicable\nIf PII collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not Applicable\nIf PII collected for the development of this AI model, was it minimized to only what was required? | Not Applicable\nIs there provenance for all datasets used in training? | Yes\nAre we able to identify and trace source of dataset? | Yes\nDoes data labeling (annotation, metadata) comply with privacy laws? | Yes, for NVIDIA collected data. \nIs data compliant with data subject requests for data correction or removal, if such a request was made? | Yes, for NVIDIA collected data.
No, for third-party vendor data.","safetyAndSecurity","Field | Response\n:---------------------------------------------------|:----------------------------------\nVerified to have met prescribed quality standards: | Yes\nTarget Key Performance Indicator(s) (KPI(s)): | Accuracy, Latency, Throughput\nModel Application(s): | Conversation, Question Answering\nDescribe the physical safety impact (if present). | None Known\nUse Case Restrictions: | Model for Non-Commerical Use Only\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",[189],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_153":190,"_191":190,"_192":193,"_194":195},"NSPECT-PVUT-J7LZ","value","filter",{"_196":195,"_191":190},"category","label","field","appConfig",{"_1375":1376,"_231":1377},"ngcApps",[1328],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1309":1310,"_1311":1312,"_1313":1314,"_1315":1316,"_1317":209,"_79":236,"_1318":1319,"_1320":44,"_1321":108,"_1322":108,"_1323":1324,"_1325":108,"_1326":1327},"amplitude",{"_1306":1307,"_908":-7,"_1308":20},"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":316,"_1184":1185,"_1186":1187,"_1188":1189},"announcements",[],"consentState",{"_273":1173,"_1174":108,"_1175":20,"_1176":20,"_1177":1178},"consentConfig",{"_255":256,"_257":258,"_259":260,"_261":262,"_263":264,"_265":266,"_267":268,"_269":270,"_271":272,"_273":274},"showContactAdmin",1784073925762,[],[],[250,251,252,253,254],{"_33":48,"_35":57,"_37":54,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":55,"_37":54,"_39":-5,"_41":-5,"_43":44},{"_33":48,"_35":49,"_37":54,"_39":-5,"_41":-5,"_43":44},{"_33":45,"_35":46,"_37":54,"_39":-5,"_41":-5,"_43":44},{"_33":34,"_35":36,"_37":38,"_39":40,"_41":47,"_43":44},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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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.",5,[425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459],[297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313],{"_314":421,"_153":421,"_111":316,"_287":422},{"_314":416,"_153":416,"_111":316,"_287":417},{"_314":412,"_153":412,"_111":316,"_287":413},{"_314":407,"_153":407,"_111":316,"_287":408},{"_314":402,"_153":402,"_111":316,"_287":403},{"_314":390,"_153":390,"_111":316,"_287":391},{"_314":378,"_153":378,"_111":316,"_287":379},{"_314":373,"_153":373,"_111":316,"_287":374},{"_314":362,"_153":362,"_111":316,"_287":363},{"_314":357,"_153":357,"_111":316,"_287":358},{"_314":352,"_153":352,"_111":316,"_287":353},{"_314":347,"_153":347,"_111":316,"_287":348},{"_314":342,"_153":342,"_111":316,"_287":343},{"_314":337,"_153":337,"_111":316,"_287":338},{"_314":332,"_153":332,"_111":316,"_287":333},{"_314":325,"_153":325,"_111":316,"_287":326},{"_314":315,"_153":315,"_111":316,"_287":317},"hostName","amazon-adsystem.com","",[318,319],{"_140":324,"_321":315,"_322":20,"_111":323},{"_140":320,"_321":315,"_322":20,"_111":323},"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",[327,328],{"_140":331,"_321":325,"_322":20,"_111":330},{"_140":329,"_321":325,"_322":20,"_111":330},"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",[354],{"_140":355,"_321":352,"_322":20,"_111":356},"sp","Testing","google.com",[359],{"_140":360,"_321":357,"_322":20,"_111":361},"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",[364,365,366,367],{"_140":372,"_321":362,"_322":20,"_111":369},{"_140":371,"_321":362,"_322":20,"_111":369},{"_140":370,"_321":362,"_322":20,"_111":369},{"_140":368,"_321":362,"_322":20,"_111":369},"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",[375],{"_140":376,"_321":373,"_322":20,"_111":377},"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",[380,381,382,383],{"_140":389,"_321":378,"_322":20,"_111":385},{"_140":387,"_321":378,"_322":20,"_111":388},{"_140":386,"_321":378,"_322":20,"_111":385},{"_140":384,"_321":378,"_322":20,"_111":385},"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",[392,393,394,395],{"_140":401,"_321":390,"_322":20,"_111":399},{"_140":400,"_321":390,"_322":20,"_111":399},{"_140":398,"_321":390,"_322":20,"_111":399},{"_140":396,"_321":390,"_322":20,"_111":397},"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",[404],{"_140":405,"_321":402,"_322":20,"_111":406},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[409],{"_140":410,"_321":407,"_322":20,"_111":411},"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",[414],{"_140":415,"_321":412,"_322":20,"_111":356},"R","6sc.co",[418],{"_140":419,"_321":416,"_322":20,"_111":420},"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.",[547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567],[512,513,514,515,516],{"_314":407,"_153":407,"_111":316,"_287":538},{"_314":373,"_153":373,"_111":316,"_287":533},{"_314":526,"_153":526,"_111":316,"_287":527},{"_314":522,"_153":522,"_111":316,"_287":523},{"_314":517,"_153":517,"_111":316,"_287":518},"nvcrm.my.site.com",[519],{"_140":520,"_321":517,"_322":20,"_111":521},"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",[524],{"_140":525,"_321":522,"_322":20,"_111":356},"edge","github.com",[528,529],{"_140":532,"_321":526,"_322":20,"_111":531},{"_140":530,"_321":526,"_322":20,"_111":531},"logged_in","This domain is owned by Github. 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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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