Nemotron-H-8B-Base-8K

Model Overview

NVIDIA Nemotron-H-8B-Base-8K is a large language model (LLM) developed by NVIDIA that is 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 four Attention layers. The model features a context length of 8K. 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:

4/14/2025

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 8B 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-8B-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 accuracy.

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 Nemotron-H-8B-Base-8K.

Data Collection for Evaluation Datasets:\nHybrid: Human, Synthetic

Data Labeling for Evaluation Datasets:\nHybrid: Human, Synthetic, Automatic

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
88.74	83.23	80.51	78.71

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
Hellaswag - Tests the ability of a language model to correctly finish the provided context from a choice of possible options. Dataset
Winogrande - Tests the ability to choose the right option for a given sentence which requires commonsense reasoning. Dataset
CommonsenseQA - A multiple-choice question answering dataset that requires different type of commonsense knowledge to predict the correct answers. Dataset

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
65.37	59.52	58.54	55.49

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
87.11	46.52	22.93	44.43

GSM8K (Grade School Math 8K) - Evaluates grade school level mathematical word problem solving. Dataset
MATH - Tests mathematical ability across multiple difficulty levels and various subjects including: Prealgebra, Algebra, Number Theory, Counting and Probability, Geometry, Intermediate Algebra, and Precalculus. 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
44.01	72.77

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

Potential Known Risks for Usage

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.

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",[269,270],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_242":24,"_243":244,"_50":245,"_246":-5,"_247":248,"_249":250,"_251":252,"_253":16,"_254":163,"_255":-5,"_256":32,"_257":-5,"_258":32,"_259":-5,"_260":32,"_261":-5,"_262":32,"_263":-5,"_264":32,"_265":-5,"_266":32,"_267":268},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/nemo/models/nemotron-h-8b-base-8k/-","version","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/nemo/nemotron-h-8b-base-8k:1.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/nemo/models/nemotron-h-8b-base-8k/-/version-history","accessCheckResult",{"_241":163},"hasRelatedCollections",{"_237":238,"_239":240},1784287732064,[],[],[229,230,231,232,233,234,235],{"_46":61,"_48":72,"_50":63,"_52":64,"_54":73,"_56":57},{"_46":61,"_48":70,"_50":63,"_52":64,"_54":71,"_56":57},{"_46":61,"_48":68,"_50":63,"_52":64,"_54":236,"_56":57},{"_46":61,"_48":66,"_50":63,"_52":64,"_54":67,"_56":57},{"_46":61,"_48":62,"_50":63,"_52":64,"_54":65,"_56":57},{"_46":58,"_48":59,"_50":51,"_52":53,"_54":60,"_56":57},{"_46":47,"_48":49,"_50":51,"_52":53,"_54":55,"_56":57},53,"orgName","nvidia","teamName","nemo","allowed","versionId","description","Nemotron-H-8B-Base-8K","UPLOAD_COMPLETE","releaseType","createdDate",["D",1744698186997],"updatedDate",["D",1744698205309],"totalSizeInBytes",16220057189,"totalFileCount","isSigned","malwareScanStatus","isScanned","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance","policies",[],{"_89":22,"_78":272,"_246":-5,"_254":163,"_256":32,"_267":271,"_258":32},{"_89":24,"_78":24,"_246":-5,"_254":163,"_256":32,"_267":271,"_258":32},[],"Latest (1.0)","nvidia/nemo/nemotron-h-8b-base-8k","key","nvidia/nemo/models/nemotron-h-8b-base-8k","resourceType","models","resourceId","nemotron-h-8b-base-8k","shortDescription","# Nemotron-H-8B-Base-8K\n\n## Model Overview \n\nNVIDIA Nemotron-H-8B-Base-8K is a large language model (LLM) developed by NVIDIA that is 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 four Attention layers. The model features a context length of 8K. 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## 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\n4/14/2025\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 8B 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:\n\nThe training corpus for Nemotron-H-8B-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 accuracy.\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 Nemotron-H-8B-Base-8K. \n\n**Data Collection for Evaluation Datasets:**\nHybrid: Human, Synthetic\n\n**Data Labeling for Evaluation Datasets:**\nHybrid: Human, Synthetic, Automatic\n\n#### Commonsense Understanding Evaluations:\n\n| ARC Challenge 25-shot | Hellaswag 10-shot | Winogrande 5-shot | CommonsenseQA 7-shot |\n|-------------|--------------|-----------------|------------------|\n| 88.74 | 83.23| 80.51 | 78.71 |\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- 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| 65.37 | 59.52| 58.54 | 55.49 |\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\n| GSM8K 8-shot CoT | MATH 4-shot CoT | MATH-Lvl 5 4-shot CoT | MATH-500 4-shot CoT |\n|--------------|------------|------------|------------|\n| 87.11 | 46.52 | 22.93 | 44.43 |\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 - Tests mathematical ability across multiple difficulty levels and various subjects including: Prealgebra, Algebra, Number Theory, Counting and Probability, Geometry, Intermediate Algebra, and Precalculus. [Dataset](https://github.com/hendrycks/math)\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\n#### General Evaluations:\n\n| MMLU-Pro 5-shot CoT | MMLU 5-shot|\n|-------------------|------------------|\n|44.01 |72.77 | \n\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- MMLU - Tests knowledge across 57 subjects including science, humanities, math and more. [Dataset](https://github.com/hendrycks/test)\n\n## Potential Known Risks for Usage\n\nThe 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.\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",1745288941293],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/nemotron-h-8b-base-8k","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Nemo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[314,315],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_282":323,"_317":323,"_318":324,"_320":321},{"_282":316,"_317":316,"_318":319,"_320":321},"NSPECT-8HQ3-4M06","value","filter",{"_322":321,"_317":316},"category","label","field","NeMo",{"_322":325,"_317":323},"framework",{"_183":1498,"_185":1499},"ngcApps",[1451],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1432":1433,"_1434":1435,"_1436":1437,"_1438":1439,"_1440":337,"_214":361,"_1441":1442,"_1443":57,"_1444":163,"_1445":163,"_1446":1447,"_1448":163,"_1449":1450},"amplitude",{"_1429":1430,"_1032":-7,"_1431":32},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1423":1424,"_1425":1426,"_1427":1428},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1307":299,"_1308":1309,"_1310":1311,"_1312":1313},"announcements",[],"consentState",{"_398":1297,"_1298":163,"_1299":32,"_1300":32,"_1301":1302},"consentConfig",{"_380":381,"_382":383,"_384":385,"_386":387,"_388":389,"_390":391,"_392":393,"_394":395,"_396":397,"_398":399},"showContactAdmin",1784287731906,[],[],[375,376,377,378,379],{"_46":61,"_48":72,"_50":69,"_52":-5,"_54":-5,"_56":57},{"_46":61,"_48":70,"_50":69,"_52":-5,"_54":-5,"_56":57},{"_46":61,"_48":62,"_50":69,"_52":-5,"_54":-5,"_56":57},{"_46":58,"_48":59,"_50":69,"_52":-5,"_54":-5,"_56":57},{"_46":47,"_48":49,"_50":69,"_52":-5,"_54":-5,"_56":57},"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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We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",[548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582],[421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437],{"_438":544,"_282":544,"_243":299,"_412":545},{"_438":539,"_282":539,"_243":299,"_412":540},{"_438":535,"_282":535,"_243":299,"_412":536},{"_438":530,"_282":530,"_243":299,"_412":531},{"_438":525,"_282":525,"_243":299,"_412":526},{"_438":513,"_282":513,"_243":299,"_412":514},{"_438":501,"_282":501,"_243":299,"_412":502},{"_438":496,"_282":496,"_243":299,"_412":497},{"_438":485,"_282":485,"_243":299,"_412":486},{"_438":480,"_282":480,"_243":299,"_412":481},{"_438":475,"_282":475,"_243":299,"_412":476},{"_438":470,"_282":470,"_243":299,"_412":471},{"_438":465,"_282":465,"_243":299,"_412":466},{"_438":460,"_282":460,"_243":299,"_412":461},{"_438":455,"_282":455,"_243":299,"_412":456},{"_438":448,"_282":448,"_243":299,"_412":449},{"_438":439,"_282":439,"_243":299,"_412":440},"hostName","amazon-adsystem.com",[441,442],{"_78":447,"_444":439,"_445":32,"_243":446},{"_78":443,"_444":439,"_445":32,"_243":446},"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",[450,451],{"_78":454,"_444":448,"_445":32,"_243":453},{"_78":452,"_444":448,"_445":32,"_243":453},"IDE","This domain is owned by Doubleclick (Google). 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It is used as a URL shortening service, which allows Twitter to collect data on how links are shared.","snowplow.com",[477],{"_78":478,"_444":475,"_445":32,"_243":479},"sp","Testing","google.com",[482],{"_78":483,"_444":480,"_445":32,"_243":484},"NID","This domain is owned by Google Inc. Although Google is primarily known as a search engine, the company provides a diverse range of products and services. Its main source of revenue however is advertising. Google tracks users extensively both through its own products and sites, and the numerous technologies embedded into many millions of websites around the world. It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","tags.srv.stackadapt.com",[487,488,489,490],{"_78":495,"_444":485,"_445":32,"_243":492},{"_78":494,"_444":485,"_445":32,"_243":492},{"_78":493,"_444":485,"_445":32,"_243":492},{"_78":491,"_444":485,"_445":32,"_243":492},"sa-user-id-v3","This domain is associated with StackAdapt, a platform for programmatic advertising, which suggests that it is used for targeting and advertising purposes.","sa-user-id","sa-user-id-v2","sa-user-id-v4","linkedin.com",[498],{"_78":499,"_444":496,"_445":32,"_243":500},"bcookie","This domain is owned by LinkedIn, the business networking platform. 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Where twitter acts as a third party host, it collects data through a range of plug-ins and integrations, that is primarily used for tracking and targeting.","guest_id_marketing","guest_id","Twitter does not currently provide information on the use of specific cookies.","guest_id_ads","youtube.com",[515,516,517,518],{"_78":524,"_444":513,"_445":32,"_243":522},{"_78":523,"_444":513,"_445":32,"_243":522},{"_78":521,"_444":513,"_445":32,"_243":522},{"_78":519,"_444":513,"_445":32,"_243":520},"VISITOR_INFO1_LIVE","This cookie is used as a unique identifier to track viewing of videos","__Secure-YNID","YouTube is a Google owned platform for hosting and sharing videos. YouTube collects user data through videos embedded in websites, which is aggregated with profile data from other Google services in order to display targeted advertising to web visitors across a broad range of their own and other websites.","__Secure-YEC","__Secure-ROLLOUT_TOKEN","demdex.net",[527],{"_78":528,"_444":525,"_445":32,"_243":529},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[532],{"_78":533,"_444":530,"_445":32,"_243":534},"receive-cookie-deprecation","This domain is owned by im-apps.net which provides information and resources about various instant messaging (IM) applications to help users make informed decisions based on their communication needs.","influ2.com",[537],{"_78":538,"_444":535,"_445":32,"_243":479},"R","6sc.co",[541],{"_78":542,"_444":539,"_445":32,"_243":543},"6suuid","This domain is associated with the company 6Sense Insights, Inc., which provides predictive analytics and account engagement services. 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It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","kndctr_F207D74D549850760A4C98C6_AdobeOrg_identity","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","ai-podcast.nvidia.com","This cookie is used to identify the user's session on the website and is often associated with analytics or personalization.","_gcl_au","Used by Google AdSense for experimenting with advertisement efficiency across websites using their services","_mkto_trk","This cookie is associated with an email marketing service provided by Marketo. This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","_gat_gtag_UA_140825383_2","Google Analytics","_zitok","This cookie name is associated with ZoomInfo which is used to maintain a unique visitor identifier and store Personally Identifiable Information.","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","AMP_91bf70adfe","Supports Amplitude product analytics.","This cookie name is associated with StackAdapt Inc. which is used to support the effective management and optimization of digital advertising campaigns by handling information such as visitor actions on websites, visited pages, time spent, page load speed, and more.","build.nvidia.com","kndctr_F207D74D549850760A4C98C6_AdobeOrg_cluster","_cs_nnnnnnnnnnnnn","This is a session cookie. 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It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","AMP_MKTG_91bf70adfe","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",4,[671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691],[636,637,638,639,640],{"_438":530,"_282":530,"_243":299,"_412":662},{"_438":496,"_282":496,"_243":299,"_412":657},{"_438":650,"_282":650,"_243":299,"_412":651},{"_438":646,"_282":646,"_243":299,"_412":647},{"_438":641,"_282":641,"_243":299,"_412":642},"nvcrm.my.site.com",[643],{"_78":644,"_444":641,"_445":32,"_243":645},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[648],{"_78":649,"_444":646,"_445":32,"_243":479},"edge","github.com",[652,653],{"_78":656,"_444":650,"_445":32,"_243":655},{"_78":654,"_444":650,"_445":32,"_243":655},"logged_in","This domain is owned by Github. 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The data is aggregated and analyzed; therefore, it remains anonymous upon use.\n","nvHeadless","This cookie is likely used to track if a user is using the site in a 'headless' non-GUI mode, which could be for accessibility reasons or for automated processes.","_biz_nA","C0002","Performance Cookies","These cookies are used to provide quantitative measures of our website visitors, such as the number of times you visit, time on page, your mouse movements, scrolling, clicks and keystroke activity on the websites; other browsing, search, or product research behavior; and what brought you to our site. These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website 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Name	Size	Updated	Actions