Nemotron-H-47B-Base-8K

Model Overview

NVIDIA Nemotron-H-47B-Base-8K is a large language model (LLM) developed by NVIDIA, designed as a completion model for a given piece of text. It uses a hybrid model architecture that consists primarily of Mamba-2 and MLP layers combined with just five Attention layers. The model is pruned and distilled from Nemotron-H-56B-Base-8K using 63B tokens, and features an 8K context length. The supported languages include: English, German, Spanish, French, Italian, Korean, Portuguese, Russian, Japanese, and Chinese. For more detailed information on the model architecture, training, and evaluation, please see the project page and the technical report.

For best performance on a given task, users are encouraged to customize the model using the NeMo Framework suite of customization tools including Parameter-Efficient Fine-Tuning (P-tuning, Adapters, LoRA, and more), and Model Alignment (SFT, SteerLM, RLHF, and more) using NeMo-Aligner.

This model is for research and development only.

This model is part of the Nemotron-H Collection. You can find the models in this family here:

License/Terms of Use

GOVERNING TERMS: Use of this model is governed by the NVIDIA Internal Scientific Research and Development Model License.

Model Developer: NVIDIA

Model Dates:

October 2024 - March 2025

Data Freshness:

September 2024

The pretraining data has a cutoff date of September 2024.

Use Case:

This model is intended for developers and researchers building LLMs.

Release Date:

4/12/2025

References

[2504.03624] Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models

Model Architecture

Architecture Type: Hybrid Mamba-Transformer
Network Architecture: Nemotron-H

This model has 47B model parameters.

Input

Input Type(s): Text
Input Format(s): String
Input Parameters: One-Dimensional (1D): Sequences
Other Properties Related to Input: Context length up to 8K. Supported languages include German, Spanish, French, Italian, Korean, Portuguese, Russian, Japanese, Chinese and English.

Output

Output Type(s): Text
Output Format: String
Output Parameters: One-Dimensional (1D): Sequences

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Software Integration

Runtime Engine(s): NeMo 25.04 custom container (nvcr.io/nvidia/nemo:25.04.nemotron-h)
Supported Hardware Microarchitecture Compatibility: NVIDIA H100-80GB, NVIDIA A100
Operating System(s): Linux

Model Version

v1.0

Prompt Format

As this is a base model, no explicit prompt format is recommended or required.

Example

To run inference, you can use the following example script within the container nvcr.io/nvidia/nemo:25.04.nemotron-h:

\\\n --tp=8 \\\n --devices=8 \\\n --num_tokens_to_generate=40 \\\n --temperature=0.001 \\\n --top_p=0.0 \\\n --top_k=1\n\">

torchrun --nproc-per-node=8 /opt/NeMo/scripts/llm/generate.py \\\n    --model_path=<PATH_TO_NEMO2_MODEL> \\\n    --tp=8 \\\n    --devices=8 \\\n    --num_tokens_to_generate=40 \\\n    --temperature=0.001 \\\n    --top_p=0.0 \\\n    --top_k=1\n

Inference may be performed in FP8 by adding the --fp8 flag to the above command.

Training, Testing, and Evaluation Datasets

Training & Testing Datasets:

The training corpus for Nemotron-H-47B-Base-8K consists of English and multilingual text (German, Spanish, French, Italian, Korean, Portuguese, Russian, Japanese, Chinese and English), as well as code. Our sources cover a variety of document types such as: webpages, dialogue, articles, and other written materials. This model was also improved using synthetic data from Qwen (Built with Qwen). The corpus spans domains including legal, math, science, finance, and more. We also include a small portion of question-answering, and alignment style data to improve model accuracies.

Data Collection for Training & Testing Datasets:\nHybrid: Automated, Human, Synthetic

Data Labeling for Training & Testing Datasets:\nHybrid: Automated, Human, Synthetic

Evaluation Datasets

We used the datasets listed in the next section to evaluate the model.

Data Collection for Training Datasets:\nHybrid: Automated, Human, Synthetic

Data Labeling for Training Datasets:\nHybrid: Automated, Human, Synthetic

Commonsense Understanding Evaluations:

\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\">

ARC Challenge 25-shot	Hellaswag 10-shot	Winogrande 5-shot	CommonsenseQA 7-shot
94.6	87.9	83.9	87.3

\n
ARC (Ai2 reasoning challenge)-Challenge - The challenge set of questions from a benchmark that contains grade-school level, multiple-choice science questions to assess question answering ability of language models. Dataset
\n
\n
Hellaswag - Tests the ability of a language model to correctly finish the provided context from a choice of possible options. Dataset
\n
\n
Winogrande - Tests the ability to choose the right option for a given sentence which requires commonsense reasoning. Dataset
\n
\n
CommonsenseQA - A multiple-choice question answering dataset that requires different type of commonsense knowledge to predict the correct answers. Dataset
\n

Coding Evaluations:

\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\">

MBPP(sanitized) 3-shot	MBPP+ 0-shot	HumanEval 0-shot	HumanEval+ 0-shot
75.9	65.6	61.0	56.1

MBPP (Mostly Basic Python Programming Problems) - Evaluates ability to generate solutions for Python programming tasks. Dataset
MBPP+ - Extended version of MBPP with additional validation. Dataset
HumanEval - Tests code generation and completion abilities in Python. Dataset

Math Evaluations:

\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\">

GSM8K 8-shot CoT	MATH 4-shot CoT	MATH-Lvl 5 4-shot CoT	MATH-500 4-shot CoT
93.3	57.4	34.2	57.9

GSM8K (Grade School Math 8K) - Evaluates grade school level mathematical word problem solving. Dataset
MATH-500 - Tests advanced mathematical problem solving across algebra, geometry, and calculus. Dataset
MATH Lvl 5 - Only the most difficult questions from the MATH dataset. Dataset
MATH-500 - Tests advanced mathematical problem solving across algebra, geometry, and calculus. Dataset

General Evaluations:

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

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

MMLU-Pro 5-shot-cot	MMLU 5-shot
61.8	83.6

MMLU - Tests knowledge across 57 subjects including science, humanities, math and more. Dataset
MMLU Pro - Evaluates language understanding models across a broad range of challenging, reasoning-focused questions across 14 diverse domains.\nDataset

Potential Known Risks for Usage

The model was trained on data that contains toxic language, unsafe content, 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.

The model demonstrates weakness to indirect prompt injection via some encodings, including Base16, Hex/ASCII, and Braille, though is more resilient than other similar models to injections using the more common Base64 vector.

Inference

Engine: NeMo
Test Hardware NVIDIA H100-80GB

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

For more detailed information on ethical considerations for this model, please see the Responsible Use Guide available at http://nvidia.com/nemotron-responsible-use.

Please report security vulnerabilities or NVIDIA AI Concerns here.

","resourceVersions",[197,198],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_179":28,"_180":181,"_52":81,"_78":-5,"_82":182,"_84":183,"_184":80,"_91":19,"_87":88,"_185":-5,"_86":34,"_186":-5,"_187":34,"_188":-5,"_189":34,"_190":-5,"_191":34,"_192":-5,"_193":34,"_194":-5,"_195":34,"_89":196},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/nemo/models/nemotron-h-47b-base-8k/-","version","-","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/nemo/nemotron-h-47b-base-8k:1.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/nemo/models/nemotron-h-47b-base-8k/-/version-history","accessCheckResult",{"_178":88},"hasRelatedCollections","context",{"_174":175,"_176":177},1788002295770,[],[],[167,168,169,170,171,172,173],{"_48":63,"_50":73,"_52":65,"_54":66,"_56":74,"_58":59},{"_48":63,"_50":72,"_52":114,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":65,"_54":66,"_56":71,"_58":59},{"_48":63,"_50":68,"_52":65,"_54":66,"_56":69,"_58":59},{"_48":63,"_50":64,"_52":65,"_54":66,"_56":67,"_58":59},{"_48":60,"_50":61,"_52":53,"_54":55,"_56":62,"_58":59},{"_48":49,"_50":51,"_52":53,"_54":55,"_56":57,"_58":59},"orgName","nvidia","teamName","nemo","allowed","versionId","description","Nemotron-H-47B-Base-8K",["D",1744698173559],["D",1744698204251],"totalSizeInBytes","malwareScanStatus","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance",[],{"_76":151,"_77":200,"_78":-5,"_87":88,"_86":34,"_89":199,"_187":34},{"_76":28,"_77":28,"_78":-5,"_87":88,"_86":34,"_89":199,"_187":34},[],"Latest (1.0)","nvidia/nemo/nemotron-h-47b-base-8k","key","nvidia/nemo/models/nemotron-h-47b-base-8k","resourceId","nemotron-h-47b-base-8k","shortDescription","# Nemotron-H-47B-Base-8K\n\n## Model Overview \n\nNVIDIA Nemotron-H-47B-Base-8K is a large language model (LLM) developed by NVIDIA, designed as a completion model for a given piece of text. It uses a hybrid model architecture that consists primarily of Mamba-2 and MLP layers combined with just five Attention layers. The model is pruned and distilled from [Nemotron-H-56B-Base-8K](https://huggingface.co/nvidia/Nemotron-H-56B-Base-8K) using 63B tokens, and features an 8K context length. The supported languages include: English, German, Spanish, French, Italian, Korean, Portuguese, Russian, Japanese, and Chinese. For more detailed information on the model architecture, training, and evaluation, please see the [project page](https://research.nvidia.com/labs/adlr/nemotronh/) and the [technical report](https://arxiv.org/abs/2504.03624).\n\nFor best performance on a given task, users are encouraged to customize the model using the [NeMo Framework](https://docs.nvidia.com/nemo-framework/index.html) suite of customization tools including Parameter-Efficient Fine-Tuning (P-tuning, Adapters, LoRA, and more), and Model Alignment (SFT, SteerLM, RLHF, and more) using [NeMo-Aligner](https://github.com/NVIDIA/NeMo-Aligner).\n\nThis model is for research and development only.\n\nThis model is part of the Nemotron-H Collection. You can find the models in this family here:\n- [Nemotron-H-56B-Base-8K](https://huggingface.co/nvidia/Nemotron-H-56B-Base-8K)\n- [Nemotron-H-47B-Base-8K](https://huggingface.co/nvidia/Nemotron-H-47B-Base-8K)\n- [Nemotron-H-8B-Base-8K](https://huggingface.co/nvidia/Nemotron-H-8B-Base-8K)\n\n\n## License/Terms of Use\n\nGOVERNING TERMS: Use of this model is governed by the [NVIDIA Internal Scientific Research and Development Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-internal-scientific-research-and-development-model-license/).\n\n**Model Developer:** NVIDIA\n\n**Model Dates:**\n\nOctober 2024 - March 2025\n\n**Data Freshness:**\n\nSeptember 2024\n\nThe pretraining data has a cutoff date of September 2024. \n\n## Use Case:\n\nThis model is intended for developers and researchers building LLMs.\n\n## Release Date:\n\n4/12/2025\n\n## References\n\n- [\\[2504.03624\\] Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models](https://arxiv.org/abs/2504.03624)\n\n## Model Architecture\n- Architecture Type: Hybrid Mamba-Transformer\n- Network Architecture: Nemotron-H\n\nThis model has 47B model parameters.\n\n## Input\n- Input Type(s): Text\n- Input Format(s): String\n- Input Parameters: One-Dimensional (1D): Sequences\n- Other Properties Related to Input: Context length up to 8K. Supported languages include German, Spanish, French, Italian, Korean, Portuguese, Russian, Japanese, Chinese and English.\n\n## Output\n- Output Type(s): Text \n- Output Format: String\n- Output Parameters: One-Dimensional (1D): Sequences \n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.\n\n## Software Integration\n- Runtime Engine(s): NeMo 25.04 custom container (`nvcr.io/nvidia/nemo:25.04.nemotron-h`)\n- Supported Hardware Microarchitecture Compatibility: NVIDIA H100-80GB, NVIDIA A100\n- Operating System(s): Linux\n\n## Model Version\n- v1.0\n\n## Prompt Format\n\nAs this is a base model, no explicit prompt format is recommended or required.\n\n### Example\n\nTo run inference, you can use the following example script within the container `nvcr.io/nvidia/nemo:25.04.nemotron-h`:\n\n```bash\ntorchrun --nproc-per-node=8 /opt/NeMo/scripts/llm/generate.py \\\n --model_path= \\\n --tp=8 \\\n --devices=8 \\\n --num_tokens_to_generate=40 \\\n --temperature=0.001 \\\n --top_p=0.0 \\\n --top_k=1\n```\n\nInference may be performed in FP8 by adding the `--fp8` flag to the above command.\n\n## Training, Testing, and Evaluation Datasets\n\n### Training & Testing Datasets:\nThe training corpus for Nemotron-H-47B-Base-8K consists of English and multilingual text (German, Spanish, French, Italian, Korean, Portuguese, Russian, Japanese, Chinese and English), as well as code. Our sources cover a variety of document types such as: webpages, dialogue, articles, and other written materials. This model was also improved using synthetic data from Qwen (Built with Qwen). The corpus spans domains including legal, math, science, finance, and more. We also include a small portion of question-answering, and alignment style data to improve model accuracies.\n\n**Data Collection for Training & Testing Datasets:**\nHybrid: Automated, Human, Synthetic\n\n**Data Labeling for Training & Testing Datasets:**\nHybrid: Automated, Human, Synthetic\n\n### Evaluation Datasets\n\nWe used the datasets listed in the next section to evaluate the model. \n\n**Data Collection for Training Datasets:**\nHybrid: Automated, Human, Synthetic\n\n**Data Labeling for Training Datasets:**\nHybrid: Automated, Human, Synthetic\n\n#### Commonsense Understanding Evaluations:\n\n| ARC Challenge 25-shot | Hellaswag 10-shot | Winogrande 5-shot | CommonsenseQA 7-shot |\n|-------------|--------------|-----------------|------------------|\n| 94.6 | 87.9 | 83.9 | 87.3 |\n\n- ARC (Ai2 reasoning challenge)-Challenge - The challenge set of questions from a benchmark that contains grade-school level, multiple-choice science questions to assess question answering ability of language models. [Dataset](https://huggingface.co/datasets/allenai/ai2_arc)\n- Hellaswag - Tests the ability of a language model to correctly finish the provided context from a choice of possible options. [Dataset](https://huggingface.co/datasets/Rowan/hellaswag )\n\n- Winogrande - Tests the ability to choose the right option for a given sentence which requires commonsense reasoning. [Dataset](https://huggingface.co/datasets/allenai/winogrande )\n- CommonsenseQA - A multiple-choice question answering dataset that requires different type of commonsense knowledge to predict the correct answers. [Dataset](https://huggingface.co/datasets/tau/commonsense_qa )\n\n#### Coding Evaluations:\n\n| MBPP(sanitized) 3-shot | MBPP+ 0-shot | HumanEval 0-shot | HumanEval+ 0-shot |\n|-------------|--------------|-----------------|------------------|\n| 75.9 | 65.6| 61.0 | 56.1 |\n\n- MBPP (Mostly Basic Python Programming Problems) - Evaluates ability to generate solutions for Python programming tasks. [Dataset](https://github.com/google-research/google-research/tree/master/mbpp)\n- MBPP+ - Extended version of MBPP with additional validation. [Dataset](https://huggingface.co/datasets/evalplus/mbppplus)\n- HumanEval - Tests code generation and completion abilities in Python. [Dataset](https://github.com/openai/human-eval)\n\n#### Math Evaluations:\n\n| GSM8K 8-shot CoT | MATH 4-shot CoT | MATH-Lvl 5 4-shot CoT | MATH-500 4-shot CoT |\n|--------------|------------|------------|------------|\n| 93.3 | 57.4 | 34.2 | 57.9 |\n\n- GSM8K (Grade School Math 8K) - Evaluates grade school level mathematical word problem solving. [Dataset](https://github.com/openai/grade-school-math)\n- MATH-500 - Tests advanced mathematical problem solving across algebra, geometry, and calculus. [Dataset](https://huggingface.co/datasets/HuggingFaceH4/MATH-500)\n- MATH Lvl 5 - Only the most difficult questions from the MATH dataset. [Dataset](https://github.com/hendrycks/math)\n- MATH-500 - Tests advanced mathematical problem solving across algebra, geometry, and calculus. [Dataset](https://huggingface.co/datasets/HuggingFaceH4/MATH-500)\n\n#### General Evaluations:\n\n| MMLU-Pro 5-shot-cot | MMLU 5-shot |\n|------------------|------------|\n| 61.8 | 83.6 |\n\n- MMLU - Tests knowledge across 57 subjects including science, humanities, math and more. [Dataset](https://github.com/hendrycks/test)\n- MMLU Pro - Evaluates language understanding models across a broad range of challenging, reasoning-focused questions across 14 diverse domains.\n[Dataset](https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro)\n\n## Potential Known Risks for Usage\n\nThe model was trained on data that contains toxic language, unsafe content, 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.\n\nThe model demonstrates weakness to indirect prompt injection via some encodings, including Base16, Hex/ASCII, and Braille, though is more resilient than other similar models to injections using the more common Base64 vector.\n\n## Inference\n- Engine: NeMo\n- Test Hardware NVIDIA H100-80GB\n\n## Ethical Considerations\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. \n\nFor more detailed information on ethical considerations for this model, please see the Responsible Use Guide available at http://nvidia.com/nemotron-responsible-use.\n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName",["D",1747075222491],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/nemotron-h-47b-base-8k","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[239,240],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_208":248,"_242":248,"_243":249,"_245":246},{"_208":241,"_242":241,"_243":244,"_245":246},"NSPECT-GMLV-MW4V","value","filter",{"_247":246,"_242":241},"category","label","field","NeMo",{"_247":250,"_242":248},"framework",{"_119":1226,"_121":1227},"ngcApps",[1179],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1160":1161,"_1162":1163,"_1164":1165,"_1166":1167,"_1168":262,"_150":288,"_1169":1170,"_1171":59,"_1172":88,"_1173":88,"_1174":1175,"_1176":88,"_1177":1178},"amplitude",{"_1157":1158,"_799":-7,"_1159":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1151":1152,"_1153":1154,"_1155":1156},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1788002295651,"1.12.0","defaultSearchSuggestions",{"_1039":224,"_1040":1041,"_1042":1043,"_1044":1045},"announcements",[],"consentState",{"_325":1029,"_1030":88,"_1031":34,"_1032":34,"_1033":1034},"consentConfig",{"_307":308,"_309":310,"_311":312,"_313":314,"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326},"showContactAdmin",1788002295652,[],[],[302,303,304,305,306],{"_48":63,"_50":73,"_52":114,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":114,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":114,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":114,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":114,"_54":-5,"_56":-5,"_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. 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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,[464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485],[349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365],{"_366":459,"_208":459,"_180":224,"_339":460},{"_366":455,"_208":455,"_180":224,"_339":456},{"_366":450,"_208":450,"_180":224,"_339":451},{"_366":446,"_208":446,"_180":224,"_339":447},{"_366":441,"_208":441,"_180":224,"_339":442},{"_366":436,"_208":436,"_180":224,"_339":437},{"_366":428,"_208":428,"_180":224,"_339":429},{"_366":420,"_208":420,"_180":224,"_339":421},{"_366":415,"_208":415,"_180":224,"_339":416},{"_366":410,"_208":410,"_180":224,"_339":411},{"_366":405,"_208":405,"_180":224,"_339":406},{"_366":400,"_208":400,"_180":224,"_339":401},{"_366":395,"_208":395,"_180":224,"_339":396},{"_366":388,"_208":388,"_180":224,"_339":389},{"_366":383,"_208":383,"_180":224,"_339":384},{"_366":376,"_208":376,"_180":224,"_339":377},{"_366":367,"_208":367,"_180":224,"_339":368},"hostName","amazon-adsystem.com",[369,370],{"_77":375,"_372":367,"_373":34,"_180":374},{"_77":371,"_372":367,"_373":34,"_180":374},"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",[378,379],{"_77":382,"_372":376,"_373":34,"_180":381},{"_77":380,"_372":376,"_373":34,"_180":381},"IDE","This domain is owned by Doubleclick (Google). 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It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","linkedin.com",[417],{"_77":418,"_372":415,"_373":34,"_180":419},"bcookie","This domain is owned by LinkedIn, the business networking platform. It typically acts as a third party host where website owners have placed one of its content sharing buttons in their pages, although its content and services can be embedded in other ways. Although such buttons add functionality to the website they are on, cookies are set regardless of whether or not the visitor has an active Linkedin profile, or agreed to their terms and conditions. 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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. 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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.",[556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580],[527,528,529,530,531],{"_366":441,"_208":441,"_180":224,"_339":551},{"_366":415,"_208":415,"_180":224,"_339":546},{"_366":541,"_208":541,"_180":224,"_339":542},{"_366":536,"_208":536,"_180":224,"_339":537},{"_366":532,"_208":532,"_180":224,"_339":533},"nvidia.partners",[534],{"_77":535,"_372":532,"_373":34,"_180":409},"edge","app.hushly.com",[538],{"_77":539,"_372":536,"_373":34,"_180":540},"*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",[543],{"_77":544,"_372":541,"_373":34,"_180":545},"_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.",[639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667],[619,620,621,622],{"_366":634,"_208":634,"_180":224,"_339":635},{"_366":629,"_208":629,"_180":224,"_339":630},{"_366":428,"_208":428,"_180":224,"_339":626},{"_366":541,"_208":541,"_180":224,"_339":623},[624],{"_77":625,"_372":541,"_373":88,"_180":545},"_gh_sess",[627],{"_77":628,"_372":428,"_373":88,"_180":435},"YSC","bizible.com",[631],{"_77":632,"_372":629,"_373":34,"_180":633},"_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",[636],{"_77":637,"_372":634,"_373":34,"_180":638},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_77":709,"_372":487,"_373":34,"_180":710},{"_77":694,"_372":708,"_373":88,"_180":695},{"_77":677,"_372":490,"_373":34,"_180":602},{"_77":677,"_372":590,"_373":34,"_180":602},{"_77":706,"_372":487,"_373":34,"_180":707},{"_77":694,"_372":493,"_373":88,"_180":695},{"_77":704,"_372":487,"_373":34,"_180":705},{"_77":694,"_372":490,"_373":88,"_180":695},{"_77":702,"_372":589,"_373":34,"_180":703},{"_77":694,"_372":611,"_373":88,"_180":695},{"_77":674,"_372":490,"_373":34,"_180":676},{"_77":700,"_372":487,"_373":88,"_180":701},{"_77":698,"_372":487,"_373":34,"_180":699},{"_77":696,"_372":487,"_373":88,"_180":697},{"_77":694,"_372":585,"_373":88,"_180":695},{"_77":692,"_372":487,"_373":34,"_180":693},{"_77":690,"_372":487,"_373":34,"_180":691},{"_77":688,"_372":487,"_373":88,"_180":689},{"_77":686,"_372":594,"_373":34,"_180":687},{"_77":674,"_372":585,"_373":34,"_180":676},{"_77":684,"_372":487,"_373":34,"_180":685},{"_77":682,"_372":487,"_373":88,"_180":683},{"_77":680,"_372":487,"_373":34,"_180":681},{"_77":678,"_372":487,"_373":34,"_180":679},{"_77":677,"_372":487,"_373":34,"_180":602},{"_77":674,"_372":675,"_373":34,"_180":676},{"_77":672,"_372":487,"_373":34,"_180":673},{"_77":670,"_372":487,"_373":88,"_180":671},{"_77":668,"_372":487,"_373":34,"_180":669},"_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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