Nemotron-3-8B-QA-4k

Model Overview

License

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

Description

Nemotron-3-8B-QA-4k is a 8 billion parameter generative language model customized on an 8B base model. It takes input with context length up to 4,096 tokens. The model has been further fine-tuned for instruction following Supervised Fine-tuning (SFT) using a method by NVIDIA specifically for Question and Answer method, to give concise and reliable answers.

Nemotron-3-8B-QA 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 framework 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)

Prompt Format

Single Turn

System: This is a chat between a user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions based on the context. The assistant should also indicate when the answer cannot be found in the context.\n\n{Context for Question-1}\n\nUser: Please give a full and complete answer for the question. {Question-1}\n\nAssistant:\n

Multi-Turn or Few-shot

System: This is a chat between a user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions based on the context. The assistant should also indicate when the answer cannot be found in the context.\n\n{Context for Question-2}\n\nUser: Please give a full and complete answer for the question. {Question-1}\n\nAssistant: {Answer-1}\n\nUser: {Question-2}\n\nAssistant:\n

Example prompt formation code

PROMPT_TEMPLATE = \"\"\"System: This is a chat between a user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions based on the context. The assistant should also indicate when the answer cannot be found in the context.\n\n{context_1}\n\nUser: Please give a full and complete answer for the question. {question_1}\n\nAssistant:\"\"\"\n\ncontext_1 = \"Climate change refers to long-term shifts in temperatures and weather patterns. Such shifts can be natural, due to changes in the sun’s activity or large volcanic eruptions. But since the 1800s, human activities have been the main driver of climate change, primarily due to the burning of fossil fuels like coal, oil and gas.\"\nquestion_1 = \"What happened in the 1800s?\"\n\nprompt = PROMPT_TEMPLATE.format(context_1=context_1, question_1=question_1)\nprint(prompt)\n

Software Integration

Runtime Engine(s):\nNVIDIA AI Enterprise

Toolkit:\nNeMo Framework

See NeMo inference container documentation for details on how to setup an inference server with the pyTriton and TensorRT-LLM backend.

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-QA-4K\")\n\n# See above for prompt format\noutput = nq.query_llm(prompts=[prompt], max_output_token=200, top_k=1, top_p=0.0, temperature=0.1)\nprint(output)\n

Supported Hardware:

H100
A100 80GB, A100 40GB

Model Version(s)

Nemotron-3-8B-QA-4k-SFT-BF16-1

Dataset

NVIDIA models are trained on a diverse set of public and proprietary datasets. This model was trained on a dataset containing 3.5 Trillion tokens of text. The dataset contains 53 different human languages and 37 programming languages. NVIDIA is committed to the responsible development of large language models and conducts reviews of all datasets included in training.

Evaluation

\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\">

Dataset and Metric	Nemotron-3-QA-8B, Zero-shot
Natural Questions, F1-Score	41.99%
Doc2Dial, BLEU-4	30.20

Intended use

The Nemotron-3-8B-QA model is best suited for Questions and Answering use cases and can be further customized on knowledge base data to achieve even better performance.

Ethical use

Technology 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.

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.

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It takes input with context length up to 4,096 tokens. The model has been further fine-tuned for instruction following Supervised Fine-tuning (SFT) using a method by NVIDIA specifically for Question and Answer method, to give concise and reliable answers.\n\nNemotron-3-8B-QA 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](registry.ngc.nvidia.com/models).\n\nNVIDIA NeMo is an end-to-end, cloud-native framework 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\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#### Prompt Format\n\n##### Single Turn\n\n```text\nSystem: This is a chat between a user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions based on the context. The assistant should also indicate when the answer cannot be found in the context.\n\n{Context for Question-1}\n\nUser: Please give a full and complete answer for the question. {Question-1}\n\nAssistant:\n```\n\n##### Multi-Turn or Few-shot\n\n```text\nSystem: This is a chat between a user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions based on the context. The assistant should also indicate when the answer cannot be found in the context.\n\n{Context for Question-2}\n\nUser: Please give a full and complete answer for the question. {Question-1}\n\nAssistant: {Answer-1}\n\nUser: {Question-2}\n\nAssistant:\n```\n\n##### Example prompt formation code\n\n```python\nPROMPT_TEMPLATE = \"\"\"System: This is a chat between a user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions based on the context. The assistant should also indicate when the answer cannot be found in the context.\n\n{context_1}\n\nUser: Please give a full and complete answer for the question. {question_1}\n\nAssistant:\"\"\"\n\ncontext_1 = \"Climate change refers to long-term shifts in temperatures and weather patterns. Such shifts can be natural, due to changes in the sun’s activity or large volcanic eruptions. But since the 1800s, human activities have been the main driver of climate change, primarily due to the burning of fossil fuels like coal, oil and gas.\"\nquestion_1 = \"What happened in the 1800s?\"\n\nprompt = PROMPT_TEMPLATE.format(context_1=context_1, question_1=question_1)\nprint(prompt)\n```\n\n#### Software Integration\n\n**Runtime Engine(s):**\nNVIDIA AI Enterprise\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 an inference server with the pyTriton and TensorRT-LLM backend.\n\n**Sample Inference Code:**\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-QA-4K\")\n\n# See above for prompt format\noutput = nq.query_llm(prompts=[prompt], max_output_token=200, top_k=1, top_p=0.0, temperature=0.1)\nprint(output)\n```\n**Supported Hardware:**\n\n- H100\n- A100 80GB, A100 40GB\n\n#### Model Version(s)\n\n`Nemotron-3-8B-QA-4k-SFT-BF16-1`\n\n### Dataset\n\nNVIDIA models are trained on a diverse set of public and proprietary datasets. This model was trained on a dataset containing 3.5 Trillion tokens of text. The dataset contains 53 different human languages and 37 programming languages. NVIDIA is committed to the responsible development of large language models and conducts reviews of all datasets included in training.\n\n### Evaluation\n\n| **Dataset and Metric** | **Nemotron-3-QA-8B, Zero-shot** |\n|:------------------------------------|:------------------------------------|\n| Natural Questions, F1-Score | 41.99% |\n| Doc2Dial, BLEU-4 | 30.20 |\n\n### Intended use\n\nThe Nemotron-3-8B-QA model is best suited for Questions and Answering use cases and can be further customized on knowledge base data to achieve even better performance.\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\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-QA-4k",["D",1731434388660],"publisher","Krzysztof_Pawelec_PL","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/nemotron-3-8b-qa-4k","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes",12634523105,"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",[246,247],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata","computedResourceType",{"_208":255,"_249":255,"_250":256,"_252":253},{"_208":248,"_249":248,"_250":251,"_252":253},"NSPECT-VAUO-0GV7","value","filter",{"_254":253,"_249":248},"category","label","field","NSPECT-DKYF-6STS",{"_254":253,"_249":255},{"_107":1391,"_109":1392},"ngcApps",[1344],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1325":1326,"_1327":1328,"_1329":1330,"_1331":1332,"_1333":268,"_138":294,"_1334":1335,"_1336":56,"_1337":87,"_1338":87,"_1339":1340,"_1341":87,"_1342":1343},"amplitude",{"_1322":1323,"_928":-7,"_1324":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1316":1317,"_1318":1319,"_1320":1321},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1791191030293,"1.19.0","defaultSearchSuggestions",{"_1198":374,"_1199":1200,"_1201":1202,"_1203":1204},"announcements",[],"consentState",{"_330":1188,"_1189":31,"_1190":31,"_1191":87,"_1192":1193},"consentConfig",{"_312":313,"_314":315,"_316":317,"_318":319,"_320":321,"_322":323,"_324":325,"_326":327,"_328":329,"_330":331},"showContactAdmin",[],[],[307,308,309,310,311],{"_45":60,"_47":71,"_49":62,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":62,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":62,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":62,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":62,"_51":-5,"_53":-5,"_55":56},"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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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,[478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515],[354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371],{"_372":473,"_208":473,"_168":374,"_344":474},{"_372":469,"_208":469,"_168":374,"_344":470},{"_372":464,"_208":464,"_168":374,"_344":465},{"_372":460,"_208":460,"_168":374,"_344":461},{"_372":455,"_208":455,"_168":374,"_344":456},{"_372":450,"_208":450,"_168":374,"_344":451},{"_372":443,"_208":443,"_168":374,"_344":444},{"_372":435,"_208":435,"_168":374,"_344":436},{"_372":430,"_208":430,"_168":374,"_344":431},{"_372":425,"_208":425,"_168":374,"_344":426},{"_372":417,"_208":417,"_168":374,"_344":418},{"_372":412,"_208":412,"_168":374,"_344":413},{"_372":407,"_208":407,"_168":374,"_344":408},{"_372":402,"_208":402,"_168":374,"_344":403},{"_372":395,"_208":395,"_168":374,"_344":396},{"_372":390,"_208":390,"_168":374,"_344":391},{"_372":383,"_208":383,"_168":374,"_344":384},{"_372":373,"_208":373,"_168":374,"_344":375},"hostName","amazon-adsystem.com","",[376,377],{"_79":382,"_379":373,"_380":31,"_168":381},{"_79":378,"_379":373,"_380":31,"_168":381},"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",[385,386],{"_79":389,"_379":383,"_380":31,"_168":388},{"_79":387,"_379":383,"_380":31,"_168":388},"IDE","This domain is owned by Doubleclick (Google). 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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.","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.","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. 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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.",[703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746],[675,676,677,678,679],{"_372":698,"_208":698,"_168":374,"_344":699},{"_372":693,"_208":693,"_168":374,"_344":694},{"_372":443,"_208":443,"_168":374,"_344":690},{"_372":683,"_208":683,"_168":374,"_344":684},{"_372":588,"_208":588,"_168":374,"_344":680},[681],{"_79":682,"_379":588,"_380":87,"_168":592},"_gh_sess","statcounter.com",[685,686],{"_79":689,"_379":683,"_380":31,"_168":688},{"_79":687,"_379":683,"_380":31,"_168":688},"is_unique","This domain is owned by StatCounter.\nThe main business activity is: Real time visitor analytics","is_visitor_unique",[691],{"_79":692,"_379":443,"_380":87,"_168":449},"YSC","bizible.com",[695],{"_79":696,"_379":693,"_380":31,"_168":697},"_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",[700],{"_79":701,"_379":698,"_380":31,"_168":702},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_79":808,"_379":517,"_380":31,"_168":809},{"_79":806,"_379":517,"_380":87,"_168":807},{"_79":803,"_379":804,"_380":31,"_168":805},{"_79":781,"_379":794,"_380":87,"_168":782},{"_79":801,"_379":517,"_380":31,"_168":802},{"_79":755,"_379":520,"_380":31,"_168":651},{"_79":755,"_379":553,"_380":31,"_168":651},{"_79":799,"_379":517,"_380":31,"_168":800},{"_79":783,"_379":638,"_380":87,"_168":784},{"_79":781,"_379":523,"_380":87,"_168":782},{"_79":797,"_379":517,"_380":31,"_168":798},{"_79":795,"_379":517,"_380":31,"_168":796},{"_79":789,"_379":517,"_380":31,"_168":374},{"_79":780,"_379":794,"_380":31,"_168":424},{"_79":781,"_379":520,"_380":87,"_168":782},{"_79":792,"_379":553,"_380":31,"_168":793},{"_79":790,"_379":643,"_380":31,"_168":791},{"_79":781,"_379":667,"_380":87,"_168":782},{"_79":789,"_379":638,"_380":31,"_168":374},{"_79":753,"_379":520,"_380":31,"_168":754},{"_79":780,"_379":520,"_380":31,"_168":424},{"_79":787,"_379":517,"_380":87,"_168":788},{"_79":785,"_379":517,"_380":31,"_168":786},{"_79":783,"_379":517,"_380":87,"_168":784},{"_79":781,"_379":638,"_380":87,"_168":782},{"_79":780,"_379":638,"_380":31,"_168":424},{"_79":778,"_379":517,"_380":31,"_168":779},{"_79":776,"_379":517,"_380":31,"_168":777},{"_79":774,"_379":517,"_380":87,"_168":775},{"_79":771,"_379":772,"_380":31,"_168":773},{"_79":753,"_379":638,"_380":31,"_168":754},{"_79":769,"_379":517,"_380":87,"_168":770},{"_79":767,"_379":517,"_380":31,"_168":768},{"_79":766,"_379":638,"_380":87,"_168":761},{"_79":764,"_379":517,"_380":87,"_168":765},{"_79":762,"_379":532,"_380":31,"_168":763},{"_79":760,"_379":638,"_380":87,"_168":761},{"_79":758,"_379":517,"_380":31,"_168":759},{"_79":756,"_379":517,"_380":31,"_168":757},{"_79":755,"_379":517,"_380":31,"_168":651},{"_79":753,"_379":532,"_380":31,"_168":754},{"_79":751,"_379":517,"_380":31,"_168":752},{"_79":749,"_379":517,"_380":87,"_168":750},{"_79":747,"_379":517,"_380":31,"_168":748},"_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","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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The cookie is used for measuring a visitor’s scroll activity to see how much of a page they view before moving on to another page.","_an_uid","This cookie is likely used for analytics purposes to track user behavior and site usage, which may include unique user identification for tracking.","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.","s_ips","_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.","s_plt","This cookie is used to measure the time taken for pages to load, allowing website owners to identify slow loading pages and improve user experience.","_session_id","resources.nvidia.com","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.","__sec_*","_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. This is done so that cookies can be shared across subdomains (where applicable). 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users logged in across NVIDIA domains.","api.ngc.nvidia.com","This cookie is managed by AWS and is used for load balancing.","bm_so","This cookie name is used to load the site as quickly as possible and to recognize bots using Akamai.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","omniverse.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","nvidiaCart","This domain is owned by NVIDIA Corporation which provides information about the company, its products, solutions, industries, news, events, and career opportunities.","nv_co","__uxq412__ses.88c1","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","learn.learn.nvidia.com","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","This cookie is associated with Akamai. It is used to analyse traffic to determine if it is automated traffic generated by bots or a human user.","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","programs.nvidia.com","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","buildapi.ngc.nvidia.com","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","_pf_consent","This cookie name is associated with Command Alkon Incorporated which is used to store user consent status and expiration.","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. It is a temporary cookie used to determine if the browser accepts cookies.","XSRF-TOKEN","This cookie is written to help with site security in preventing Cross-Site Request Forgery attacks.This cookie helps with site security in preventing Cross-Site Request Forgery attacks.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","bm_ss","This cookie is used to help protect websites from automated bot traffic.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","c_code","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","com.adobe.alloy.getTld","This cookie name is associated with the Adobe WebSDK which is used to determine the top-most domain that can accept cookies.","session","This cookie is typically used to maintain an anonymized user session by the server.","SESSls","This cookie is likely to be a session identifier, used to maintain a user's session state across page requests.","BIGipServerx","This cookie name is associated with the BIG-IP product suite from company F5. Usually associated with managing sessions on load balanced servers, to ensure user requests are routed consistently to the correct server. 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