Nemotron-3-8B-Chat-4k-SteerLM

Model Overview

License

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

Description

Nemotron-3-8B-SteerLM is an 8 billion parameter generative language model instruct-tuned on an 8B base model. It takes input with context length up to 4,096 tokens. The model has been customized using the SteerLM method developed by NVIDIA to allow for user control of model outputs during inference.\n

Key capabilities enabled by SteerLM:\n

Dynamic steering of responses by specifying desired attributes like quality, helpfulness, and toxicity at inference time.
Simplified training compared to RLHF techniques like fine-tuning and bootstrapping.

\nNemotron-3-8B-SteerLM 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.\n

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.\n

References

Announcement Blog

Model Architecture

Architecture Type: Transformer

\nNetwork Architecture: Generative Pre-Trained Transformer (GPT-3)

\nThe SteerLM method involves the following key steps:\n

Train an attribute prediction model on human annotated data to evaluate response quality.
Use this model to annotate diverse datasets and enrich training data.
Perform conditioned fine-tuning to align responses with specified combinations of attributes.
(Optionally) Bootstrap training through model sampling and further fine-tuning.

\nSteerLM-8B applies this technique on top of the open-source NVIDIA GPT model architecture. It was pretrained on internet-scale data and then customized using OASST, HH-RLHF, Light, a subset of permissive licensed OpenPlatypus, and some internally collected SFT data.

Prompt Format

Single Turn

<extra_id_0>System\nA chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\n\n<extra_id_1>User\n{prompt 1}\n<extra_id_1>Assistant\n<extra_id_2>quality:4,understanding:4,correctness:4,coherence:4,complexity:4,verbosity:4,toxicity:0,humor:0,creativity:0,violence:0,helpfulness:4,not_appropriate:0,hate_speech:0,sexual_content:0,fails_task:0,political_content:0,moral_judgement:0,lang:en\n

Multi-Turn or Few-shot

System\nA chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\n\nUser\n{prompt 1}\nAssistant\nquality:4,understanding:4,correctness:4,coherence:4,complexity:4,verbosity:4,toxicity:0,humor:0,creativity:0,violence:0,helpfulness:4,not_appropriate:0,hate_speech:0,sexual_content:0,fails_task:0,political_content:0,moral_judgement:0,lang:en\n{response 1}\nUser\n{prompt 2}\nAssistant\nquality:4,understanding:4,correctness:4,coherence:4,complexity:4,verbosity:4,toxicity:0,humor:0,creativity:0,violence:0,helpfulness:4,not_appropriate:0,hate_speech:0,sexual_content:0,fails_task:0,political_content:0,moral_judgement:0,lang:en\n\">

<extra_id_0>System\nA chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\n\n<extra_id_1>User\n{prompt 1}\n<extra_id_1>Assistant\n<extra_id_2>quality:4,understanding:4,correctness:4,coherence:4,complexity:4,verbosity:4,toxicity:0,humor:0,creativity:0,violence:0,helpfulness:4,not_appropriate:0,hate_speech:0,sexual_content:0,fails_task:0,political_content:0,moral_judgement:0,lang:en\n{response 1}\n<extra_id_1>User\n{prompt 2}\n<extra_id_1>Assistant\n<extra_id_2>quality:4,understanding:4,correctness:4,coherence:4,complexity:4,verbosity:4,toxicity:0,humor:0,creativity:0,violence:0,helpfulness:4,not_appropriate:0,hate_speech:0,sexual_content:0,fails_task:0,political_content:0,moral_judgement:0,lang:en\n

Example prompt formation code

System\nA chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\n\nUser\n{prompt}\nAssistant\nquality:4,understanding:4,correctness:4,coherence:4,complexity:4,verbosity:4,toxicity:0,humor:0,creativity:0,violence:0,helpfulness:4,not_appropriate:0,hate_speech:0,sexual_content:0,fails_task:0,political_content:0,moral_judgement:0,lang:en"""\n\nquestion = "Write a poem on NVIDIA in the style of Shakespeare"\nprompt = PROMPT_TEMPLATE.format(prompt=question)\nprint(prompt)\n\">

PROMPT_TEMPLATE = \"\"\"<extra_id_0>System\nA chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\n\n<extra_id_1>User\n{prompt}\n<extra_id_1>Assistant\n<extra_id_2>quality:4,understanding:4,correctness:4,coherence:4,complexity:4,verbosity:4,toxicity:0,humor:0,creativity:0,violence:0,helpfulness:4,not_appropriate:0,hate_speech:0,sexual_content:0,fails_task:0,political_content:0,moral_judgement:0,lang:en\"\"\"\n\nquestion = \"Write a poem on NVIDIA in the style of Shakespeare\"\nprompt = PROMPT_TEMPLATE.format(prompt=question)\nprint(prompt)\n

\nEach of the properties (e.g. humor, toxicity…) can receive integer values in the range [0,4].\n\n

Software Integration

Runtime Engine(s):\nNVIDIA AI Enterprise\n

Toolkit:\nNeMo Framework\n

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

Sample Inference Code:

token.\noutput = [[s.split("", 1)[0].strip() for s in out] for out in output]\n\nprint(output)\n\">

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-Chat-4K-RLHF\")\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)\n\n# NOTE: Chat models require post-processing the output since the `NemoQuery` API\n# does not support stopping generation on the special <extra_id_1> token.\noutput = [[s.split(\"<extra_id_1>\", 1)[0].strip() for s in out] for out in output]\n\nprint(output)\n

Supported Hardware:\n

H100
A100 80GB, A100 40GB\n

Model Version(s)

\nNemotron-3-8B-chat-4k-steerlm-BF16-1\n

Dataset

NVIDIA models are trained on a diverse set of public and proprietary datasets. NVIDIA is committed to the responsible development of large language models and conducts reviews of all datasets included in training.

Evaluation

\nMT Bench Score

\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\">

Category	Score
Total	5.6
Writing	6.35
Roleplay	6.9
Extraction	5.25
Stem	7.5
Humanities	9.02
Reasoning	4.9
Math	2.0
Coding	2.9

Intended use

\nThe 8B-Chat-SteerLM model is for users who want to customize a model’s response during inference.\n

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.

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It takes input with context length up to 4,096 tokens. The model has been customized using the [SteerLM method](https://arxiv.org/abs/2310.05344) developed by NVIDIA to allow for user control of model outputs during inference.\n\n\nKey capabilities enabled by SteerLM:\n\n- Dynamic steering of responses by specifying desired attributes like quality, helpfulness, and toxicity at inference time.\n- Simplified training compared to RLHF techniques like fine-tuning and bootstrapping.\n\n\nNemotron-3-8B-SteerLM 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\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[Announcement Blog](https://developer.nvidia.com/blog/nvidia-ai-foundation-models-build-custom-enterprise-chatbots-and-co-pilots-with-production-ready-llms/)\n#### Model Architecture\n**Architecture Type:** Transformer\n\n\n**Network Architecture:** Generative Pre-Trained Transformer (GPT-3)\n\n\nThe SteerLM method involves the following key steps:\n\n1. Train an attribute prediction model on human annotated data to evaluate response quality.\n2. Use this model to annotate diverse datasets and enrich training data.\n3. Perform conditioned fine-tuning to align responses with specified combinations of attributes.\n4. (Optionally) Bootstrap training through model sampling and further fine-tuning.\n\n\nSteerLM-8B applies this technique on top of the open-source NVIDIA GPT model architecture. It was pretrained on internet-scale data and then customized using [OASST](https://huggingface.co/datasets/OpenAssistant/oasst1), [HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf), [Light](https://github.com/facebookresearch/ParlAI/blob/9974b947fb2e801dc5608f495828532c2a714742/parlai/tasks/light_dialog/build.py#L14), a subset of permissive licensed [OpenPlatypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus), and some internally collected SFT data.\n\n#### Prompt Format\n##### Single Turn\n```text\nSystem\nA chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\n\nUser\n{prompt 1}\nAssistant\nquality:4,understanding:4,correctness:4,coherence:4,complexity:4,verbosity:4,toxicity:0,humor:0,creativity:0,violence:0,helpfulness:4,not_appropriate:0,hate_speech:0,sexual_content:0,fails_task:0,political_content:0,moral_judgement:0,lang:en\n```\n##### Multi-Turn or Few-shot\n```text\nSystem\nA chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\n\nUser\n{prompt 1}\nAssistant\nquality:4,understanding:4,correctness:4,coherence:4,complexity:4,verbosity:4,toxicity:0,humor:0,creativity:0,violence:0,helpfulness:4,not_appropriate:0,hate_speech:0,sexual_content:0,fails_task:0,political_content:0,moral_judgement:0,lang:en\n{response 1}\nUser\n{prompt 2}\nAssistant\nquality:4,understanding:4,correctness:4,coherence:4,complexity:4,verbosity:4,toxicity:0,humor:0,creativity:0,violence:0,helpfulness:4,not_appropriate:0,hate_speech:0,sexual_content:0,fails_task:0,political_content:0,moral_judgement:0,lang:en\n```\n##### Example prompt formation code\n```python\nPROMPT_TEMPLATE = \"\"\"System\nA chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\n\nUser\n{prompt}\nAssistant\nquality:4,understanding:4,correctness:4,coherence:4,complexity:4,verbosity:4,toxicity:0,humor:0,creativity:0,violence:0,helpfulness:4,not_appropriate:0,hate_speech:0,sexual_content:0,fails_task:0,political_content:0,moral_judgement:0,lang:en\"\"\"\n\nquestion = \"Write a poem on NVIDIA in the style of Shakespeare\"\nprompt = PROMPT_TEMPLATE.format(prompt=question)\nprint(prompt)\n```\n\nEach of the properties (e.g. humor, toxicity…) can receive integer values in the range `[0,4]`.\n\n\n#### Software Integration\n**Runtime Engine(s):**\nNVIDIA AI Enterprise\n\n\n**Toolkit:**\nNeMo Framework\n\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\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-Chat-4K-RLHF\")\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)\n\n# NOTE: Chat models require post-processing the output since the `NemoQuery` API\n# does not support stopping generation on the special token.\noutput = [[s.split(\"\", 1)[0].strip() for s in out] for out in output]\n\nprint(output)\n```\n**Supported Hardware:**\n\n- H100\n- A100 80GB, A100 40GB\n \n#### Model Version(s)\n\n`Nemotron-3-8B-chat-4k-steerlm-BF16-1`\n\n### Dataset\nNVIDIA models are trained on a diverse set of public and proprietary datasets. NVIDIA is committed to the responsible development of large language models and conducts reviews of all datasets included in training.\n### Evaluation\n\nMT Bench Score\n| **Category** | **Score** |\n|:---------------------|:------------------|\n| Total | 5.6 |\n| Writing | 6.35 |\n| Roleplay | 6.9 |\n| Extraction | 5.25 |\n| Stem | 7.5 |\n| Humanities | 9.02 |\n| Reasoning | 4.9 |\n| Math | 2.0 |\n| Coding | 2.9 |\n\n\n### Intended use\n\nThe 8B-Chat-SteerLM model is for users who want to customize a model’s response during inference.\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-Chat-4k-SteerLM",["D",1731434354829],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/nemotron-3-8b-chat-4k-steerlm","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes",13693111268,"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",[245,246],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_209":254,"_248":254,"_249":255,"_251":252},{"_209":247,"_248":247,"_249":250,"_251":252},"NSPECT-OWPC-PZIO","value","filter",{"_253":252,"_248":247},"category","label","field","NSPECT-9MK3-DFBG",{"_253":252,"_248":254},{"_119":1237,"_121":1238},"ngcApps",[1190],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1171":1172,"_1173":1174,"_1175":1176,"_1177":1178,"_1179":267,"_150":293,"_1180":1181,"_1182":59,"_1183":98,"_1184":98,"_1185":1186,"_1187":98,"_1188":1189},"amplitude",{"_1168":1169,"_806":-7,"_1170":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1162":1163,"_1164":1165,"_1166":1167},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1789299787450,"1.15.0","defaultSearchSuggestions",{"_1046":373,"_1047":1048,"_1049":1050,"_1051":1052},"announcements",[],"consentState",{"_330":1036,"_1037":34,"_1038":34,"_1039":98,"_1040":1041},"consentConfig",{"_312":313,"_314":315,"_316":317,"_318":319,"_320":321,"_322":323,"_324":325,"_326":327,"_328":329,"_330":331},"showContactAdmin",1789299787451,[],[],[307,308,309,310,311],{"_48":62,"_50":73,"_52":114,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":69,"_52":114,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":63,"_52":114,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":53,"_54":55,"_56":57,"_58":59},{"_48":49,"_50":51,"_52":53,"_54":55,"_56":57,"_58":59},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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,[470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491],[354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370],{"_371":465,"_209":465,"_180":373,"_344":466},{"_371":461,"_209":461,"_180":373,"_344":462},{"_371":456,"_209":456,"_180":373,"_344":457},{"_371":452,"_209":452,"_180":373,"_344":453},{"_371":447,"_209":447,"_180":373,"_344":448},{"_371":442,"_209":442,"_180":373,"_344":443},{"_371":434,"_209":434,"_180":373,"_344":435},{"_371":426,"_209":426,"_180":373,"_344":427},{"_371":421,"_209":421,"_180":373,"_344":422},{"_371":416,"_209":416,"_180":373,"_344":417},{"_371":411,"_209":411,"_180":373,"_344":412},{"_371":406,"_209":406,"_180":373,"_344":407},{"_371":401,"_209":401,"_180":373,"_344":402},{"_371":394,"_209":394,"_180":373,"_344":395},{"_371":389,"_209":389,"_180":373,"_344":390},{"_371":382,"_209":382,"_180":373,"_344":383},{"_371":372,"_209":372,"_180":373,"_344":374},"hostName","amazon-adsystem.com","",[375,376],{"_77":381,"_378":372,"_379":34,"_180":380},{"_77":377,"_378":372,"_379":34,"_180":380},"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",[384,385],{"_77":388,"_378":382,"_379":34,"_180":387},{"_77":386,"_378":382,"_379":34,"_180":387},"IDE","This domain is owned by Doubleclick (Google). 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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.",4,[563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587],[534,535,536,537,538],{"_371":447,"_209":447,"_180":373,"_344":558},{"_371":421,"_209":421,"_180":373,"_344":553},{"_371":548,"_209":548,"_180":373,"_344":549},{"_371":543,"_209":543,"_180":373,"_344":544},{"_371":539,"_209":539,"_180":373,"_344":540},"nvidia.partners",[541],{"_77":542,"_378":539,"_379":34,"_180":415},"edge","app.hushly.com",[545],{"_77":546,"_378":543,"_379":34,"_180":547},"*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",[550],{"_77":551,"_378":548,"_379":34,"_180":552},"_octo","This domain is owned by Github. 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We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","nv_country_code","developer.nvidia.com","This cookie is likely used to store the country code of the visitor to provide localized content and possibly for legal compliance with regional data privacy laws.","_gdxxxxxxxxxxxxx","nvidianews.nvidia.com","docs.api.nvidia.com","research.nvidia.com","readme_library","wcmmode","ai-podcast.nvidia.com","resources.nvidia.com","geo_locator","openedx-language-preference","learn.nvidia.com","doxygen_width","raytracing-docs.nvidia.com","This cookie is likely used to remember the width preferences of a sidebar or interface element within a documentation platform like Doxygen.","_sfid_eb78","This cookie is categorized as Functionality Cookie.","crowdsource.nvidia.com","mbox","Cookie set by Adobe SiteCatalyst analytics software - used for measuring the performance of page content using A/B split testing","readme_language","vid","This cookie name 'vid' is used for tracking the search request made by the user on the 'Find a practitioner' page.","at_check","Used by Adobe Target to check if cookies are enabled/supported on the browser","blogs.nvidia.com","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.","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 performance.",[646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674],[626,627,628,629],{"_371":641,"_209":641,"_180":373,"_344":642},{"_371":636,"_209":636,"_180":373,"_344":637},{"_371":434,"_209":434,"_180":373,"_344":633},{"_371":548,"_209":548,"_180":373,"_344":630},[631],{"_77":632,"_378":548,"_379":98,"_180":552},"_gh_sess",[634],{"_77":635,"_378":434,"_379":98,"_180":441},"YSC","bizible.com",[638],{"_77":639,"_378":636,"_379":34,"_180":640},"_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",[643],{"_77":644,"_378":641,"_379":34,"_180":645},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_77":716,"_378":493,"_379":34,"_180":717},{"_77":701,"_378":715,"_379":98,"_180":702},{"_77":684,"_378":496,"_379":34,"_180":609},{"_77":684,"_378":597,"_379":34,"_180":609},{"_77":713,"_378":493,"_379":34,"_180":714},{"_77":701,"_378":499,"_379":98,"_180":702},{"_77":711,"_378":493,"_379":34,"_180":712},{"_77":701,"_378":496,"_379":98,"_180":702},{"_77":709,"_378":596,"_379":34,"_180":710},{"_77":701,"_378":618,"_379":98,"_180":702},{"_77":681,"_378":496,"_379":34,"_180":683},{"_77":707,"_378":493,"_379":98,"_180":708},{"_77":705,"_378":493,"_379":34,"_180":706},{"_77":703,"_378":493,"_379":98,"_180":704},{"_77":701,"_378":592,"_379":98,"_180":702},{"_77":699,"_378":493,"_379":34,"_180":700},{"_77":697,"_378":493,"_379":34,"_180":698},{"_77":695,"_378":493,"_379":98,"_180":696},{"_77":693,"_378":601,"_379":34,"_180":694},{"_77":681,"_378":592,"_379":34,"_180":683},{"_77":691,"_378":493,"_379":34,"_180":692},{"_77":689,"_378":493,"_379":98,"_180":690},{"_77":687,"_378":493,"_379":34,"_180":688},{"_77":685,"_378":493,"_379":34,"_180":686},{"_77":684,"_378":493,"_379":34,"_180":609},{"_77":681,"_378":682,"_379":34,"_180":683},{"_77":679,"_378":493,"_379":34,"_180":680},{"_77":677,"_378":493,"_379":98,"_180":678},{"_77":675,"_378":493,"_379":34,"_180":676},"_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. 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