Llama-3.1-Nemotron-Nano-8B-v1

Model Overview

Llama-3.1-Nemotron-Nano-8B-v1 is a large language model (LLM) which is a derivative of Meta Llama-3.1-8B-Instruct (AKA the reference model). It is a reasoning model that is post trained for reasoning, human chat preferences, and tasks, such as RAG and tool calling.

Llama-3.1-Nemotron-Nano-8B-v1 is a model which offers a great tradeoff between model accuracy and efficiency. It is created from Llama 3.1 8B Instruct and offers improvements in model accuracy. The model fits on a single RTX GPU and can be used locally. The model supports a context length of 128K.

This model underwent a multi-phase post-training process to enhance both its reasoning and non-reasoning capabilities. This includes a supervised fine-tuning stage for Math, Code, Reasoning, and Tool Calling as well as multiple reinforcement learning (RL) stages using REINFORCE (RLOO) and Online Reward-aware Preference Optimization (RPO) algorithms for both chat and instruction-following. The final model checkpoint is obtained after merging the final SFT and Online RPO checkpoints.

This model is part of the Llama Nemotron Collection. You can find the other model(s) in this family here:\nLlama-3_3-Nemotron-Super-49B-v1

This model is ready for commercial use.

License/Terms of Use

GOVERNING TERMS: Your use of this model is governed by the NVIDIA Open Model License. Additional Information: Llama 3.1 Community License Agreement. Built with Llama.

Model Developer: NVIDIA

Model Dates: Trained between August 2024 and March 2025

Data Freshness: The pretraining data has a cutoff of 2023 per Meta Llama 3.1 8B

Use Case:

Developers designing AI Agent systems, chatbots, RAG systems, and other AI-powered applications. Also suitable for typical instruction-following tasks. Balance of model accuracy and compute efficiency (the model fits on a single RTX GPU and can be used locally).

Release Date:

3/18/2025

References

[2502.00203] Reward-aware Preference Optimization: A Unified Mathematical Framework for Model Alignment

Model Architecture

Architecture Type: Dense decoder-only Transformer model\nNetwork Architecture: Llama 3.1 8B Instruct

Intended use

Llama-3.1-Nemotron-Nano-8B-v1 is a general purpose reasoning and chat model intended to be used in English and coding languages. Other non-English languages (German, French, Italian, Portuguese, Hindi, Spanish, and Thai) are also supported.

Input:

Input Type: Text
Input Format: String
Input Parameters: One-Dimensional (1D)
Other Properties Related to Input: Context length up to 131,072 tokens

Output:

Output Type: Text
Output Format: String
Output Parameters: One-Dimensional (1D)
Other Properties Related to Output: Context length up to 131,072 tokens

Model Version:

1.0 (3/18/2025)

Software Integration

Runtime Engine: NeMo 24.12
Recommended Hardware Microarchitecture Compatibility:\n
- NVIDIA Hopper
- NVIDIA Ampere
\n

Quick Start and Usage Recommendations:

Reasoning mode (ON/OFF) is controlled via the system prompt, which must be set as shown in the example below. All instructions should be contained within the user prompt
We recommend setting temperature to 0.6, and Top P to 0.95 for Reasoning ON mode
We recommend using greedy decoding for Reasoning OFF mode
We have provided a list of prompts to use for evaluation for each benchmark where a specific template is required
The model will include <think></think> if no reasoning was necessary in Reasoning ON model, this is expected behaviour

You can try this model out through the preview API, using this link: Llama-3_1-Nemotron-Nano-8B-v1.

Inference:

Engine: Transformers\nTest Hardware:

BF16:\n
- 1x RTX 50 Series GPUs
- 1x RTX 40 Series GPUs
- 1x RTX 30 Series GPUs
- 1x H100-80GB GPU
- 1x A100-80GB GPU
\n

Preferred/Supported] Operating System(s): Linux

Training Datasets

A large variety of training data was used for the post-training pipeline, including manually annotated data and synthetic data.

The data for the multi-stage post-training phases for improvements in Code, Math, and Reasoning is a compilation of SFT and RL data that supports improvements of math, code, general reasoning, and instruction following capabilities of the original Llama instruct model.

Prompts have been sourced from either public and open corpus or synthetically generated. Responses were synthetically generated by a variety of models, with some prompts containing responses for both Reasoning On and Off modes, to train the model to distinguish between two modes.

Data Collection for Training Datasets: Hybrid: Automated, Human, Synthetic

Data Labeling for Training Datasets: N/A

Evaluation Datasets

We used the datasets listed below to evaluate Llama-3.1-Nemotron-Nano-8B-v1.

Data Collection for Evaluation Datasets: Hybrid: Human/Synthetic

Data Labeling for Evaluation Datasets: Hybrid: Human/Synthetic/Automatic

Evaluation Results

These results contain both “Reasoning On”, and “Reasoning Off”. We recommend using temperature=0.6, top_p=0.95 for “Reasoning On” mode, and greedy decoding for “Reasoning Off” mode. All evaluations are done with 32k sequence length. We run the benchmarks up to 16 times and average the scores to be more accurate.

\n
NOTE: Where applicable, a Prompt Template will be provided. While completing benchmarks, please ensure that you are parsing for the correct output format as per the provided prompt in order to reproduce the benchmarks seen below.
\n

MT-Bench

\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\">

Reasoning Mode	Score
Reasoning Off	7.9
Reasoning On	8.1

MATH500

\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\">

Reasoning Mode	pass@1
Reasoning Off	36.6%
Reasoning On	95.4%

User Prompt Template:

\"Below is a math question. I want you to reason through the steps and then give a final answer. Your final answer should be in \\boxed{}.\\nQuestion: {question}\"\n

AIME25

\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\">

Reasoning Mode	pass@1
Reasoning Off	36.6%
Reasoning On	47.1%

User Prompt Template:

\"Below is a math question. I want you to reason through the steps and then give a final answer. Your final answer should be in \\boxed{}.\\nQuestion: {question}\"\n

GPQA-D

\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\">

Reasoning Mode	pass@1
Reasoning Off	39.4%
Reasoning On	54.1%

User Prompt Template:

\"What is the correct answer to this question: {question}\\nChoices:\\nA. {option_A}\\nB. {option_B}\\nC. {option_C}\\nD. {option_D}\\nLet's think step by step, and put the final answer (should be a single letter A, B, C, or D) into a \\boxed{}\"\n

IFEval Average

\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\">

Reasoning Mode	Strict:Prompt	Strict:Instruction
Reasoning Off	74.7%	82.1%
Reasoning On	71.9%	79.3%

BFCL v2 Live

\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\">

Reasoning Mode	Score
Reasoning Off	63.9%
Reasoning On	63.6%

User Prompt Template:

{functions}\n\n{user_prompt}\n\">

<AVAILABLE_TOOLS>{functions}</AVAILABLE_TOOLS>\n\n{user_prompt}\n

MBPP 0-shot

\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\">

Reasoning Mode	pass@1
Reasoning Off	66.1%
Reasoning On	84.6%

User Prompt Template:

You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.\n\n@@ Instruction\nHere is the given problem and test examples:\n{prompt}\nPlease use the python programming language to solve this problem.\nPlease make sure that your code includes the functions from the test samples and that the input and output formats of these functions match the test samples.\nPlease return all completed codes in one code block.\nThis code block should be in the following format:\n```python\n# Your codes here\n```\n

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 Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards, which you can find by clicking the ModelCard++ tab above, next to Overview.

Please report security vulnerabilities or NVIDIA AI Concerns here.

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\n3/18/2025
\n\n## References\n* [2502.00203] [Reward-aware Preference Optimization: A Unified Mathematical Framework for Model Alignment](https://arxiv.org/abs/2502.00203)\n\n\n## Model Architecture\n**Architecture Type:** Dense decoder-only Transformer model\n**Network Architecture:** Llama 3.1 8B Instruct\n\n## Intended use\n\nLlama-3.1-Nemotron-Nano-8B-v1 is a general purpose reasoning and chat model intended to be used in English and coding languages. Other non-English languages (German, French, Italian, Portuguese, Hindi, Spanish, and Thai) are also supported.\n\n## Input:\n- **Input Type:** Text\n- **Input Format:** String\n- **Input Parameters:** One-Dimensional (1D)\n- **Other Properties Related to Input:** Context length up to 131,072 tokens\n\n## Output:\n- **Output Type:** Text\n- **Output Format:** String\n- **Output Parameters:** One-Dimensional (1D)\n- **Other Properties Related to Output:** Context length up to 131,072 tokens\n\n## Model Version:\n1.0 (3/18/2025)\n\n## Software Integration\n- **Runtime Engine:** NeMo 24.12
\n- **Recommended Hardware Microarchitecture Compatibility:**\n - NVIDIA Hopper\n - NVIDIA Ampere\n\n## Quick Start and Usage Recommendations:\n\n1. Reasoning mode (ON/OFF) is controlled via the system prompt, which must be set as shown in the example below. All instructions should be contained within the user prompt\n2. We recommend setting temperature to `0.6`, and Top P to `0.95` for Reasoning ON mode\n3. We recommend using greedy decoding for Reasoning OFF mode\n4. We have provided a list of prompts to use for evaluation for each benchmark where a specific template is required\n5. The model will include `` if no reasoning was necessary in Reasoning ON model, this is expected behaviour\n\nYou can try this model out through the preview API, using this link: [Llama-3_1-Nemotron-Nano-8B-v1](https://build.nvidia.com/nvidia/llama-3_1-nemotron-nano-8b-v1).\n\n## Inference:\n**Engine:** Transformers\n**Test Hardware:**\n- BF16:\n - 1x RTX 50 Series GPUs\n - 1x RTX 40 Series GPUs\n - 1x RTX 30 Series GPUs\n - 1x H100-80GB GPU\n - 1x A100-80GB GPU\n\n\n\n**Preferred/Supported] Operating System(s):** Linux
\n\n## Training Datasets\n\nA large variety of training data was used for the post-training pipeline, including manually annotated data and synthetic data.\n\nThe data for the multi-stage post-training phases for improvements in Code, Math, and Reasoning is a compilation of SFT and RL data that supports improvements of math, code, general reasoning, and instruction following capabilities of the original Llama instruct model.\n\nPrompts have been sourced from either public and open corpus or synthetically generated. Responses were synthetically generated by a variety of models, with some prompts containing responses for both Reasoning On and Off modes, to train the model to distinguish between two modes.\n\n**Data Collection for Training Datasets:** Hybrid: Automated, Human, Synthetic
\n\n**Data Labeling for Training Datasets:** N/A
\n\n## Evaluation Datasets\n\nWe used the datasets listed below to evaluate Llama-3.1-Nemotron-Nano-8B-v1.\n\n**Data Collection for Evaluation Datasets:** Hybrid: Human/Synthetic\n\n**Data Labeling for Evaluation Datasets:** Hybrid: Human/Synthetic/Automatic\n\n## Evaluation Results\n\nThese results contain both “Reasoning On”, and “Reasoning Off”. We recommend using temperature=`0.6`, top_p=`0.95` for “Reasoning On” mode, and greedy decoding for “Reasoning Off” mode. All evaluations are done with 32k sequence length. We run the benchmarks up to 16 times and average the scores to be more accurate.\n\n> NOTE: Where applicable, a Prompt Template will be provided. While completing benchmarks, please ensure that you are parsing for the correct output format as per the provided prompt in order to reproduce the benchmarks seen below.\n\n### MT-Bench\n\n| Reasoning Mode | Score |\n|--------------|------------|\n| Reasoning Off | 7.9 |\n| Reasoning On | 8.1 |\n\n\n### MATH500\n\n| Reasoning Mode | pass@1 |\n|--------------|------------|\n| Reasoning Off | 36.6% |\n| Reasoning On | 95.4% |\n\nUser Prompt Template:\n```\n\"Below is a math question. I want you to reason through the steps and then give a final answer. Your final answer should be in \\boxed{}.\\nQuestion: {question}\"\n```\n\n### AIME25\n\n| Reasoning Mode | pass@1 |\n|--------------|------------|\n| Reasoning Off | 36.6% |\n| Reasoning On | 47.1% |\n\nUser Prompt Template:\n```\n\"Below is a math question. I want you to reason through the steps and then give a final answer. Your final answer should be in \\boxed{}.\\nQuestion: {question}\"\n```\n\n### GPQA-D\n\n| Reasoning Mode | pass@1 |\n|--------------|------------|\n| Reasoning Off | 39.4% |\n| Reasoning On | 54.1% |\n\nUser Prompt Template:\n```\n\"What is the correct answer to this question: {question}\\nChoices:\\nA. {option_A}\\nB. {option_B}\\nC. {option_C}\\nD. {option_D}\\nLet's think step by step, and put the final answer (should be a single letter A, B, C, or D) into a \\boxed{}\"\n```\n\n### IFEval Average\n\n| Reasoning Mode | Strict:Prompt | Strict:Instruction |\n|--------------|------------|------------|\n| Reasoning Off | 74.7% | 82.1% |\n| Reasoning On | 71.9% | 79.3% |\n\n### BFCL v2 Live\n\n| Reasoning Mode | Score |\n|--------------|------------|\n| Reasoning Off | 63.9% |\n| Reasoning On | 63.6% |\n\nUser Prompt Template:\n```\n{functions}\n\n{user_prompt}\n```\n\n### MBPP 0-shot\n\n| Reasoning Mode | pass@1 |\n|--------------|------------|\n| Reasoning Off | 66.1% |\n| Reasoning On | 84.6% |\n\nUser Prompt Template:\n````\nYou are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.\n\n@@ Instruction\nHere is the given problem and test examples:\n{prompt}\nPlease use the python programming language to solve this problem.\nPlease make sure that your code includes the functions from the test samples and that the input and output formats of these functions match the test samples.\nPlease return all completed codes in one code block.\nThis code block should be in the following format:\n```python\n# Your codes here\n```\n````\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 Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards, which you can find by clicking the ModelCard++ tab above, next to Overview.\n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","Nemotron Nano - LLaMa 3.1 - 8b",["D",1749515539544],"publisher","Nvidia","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/nemo/models/nemotron-nano-3_1-8b","rawImageUrl","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[325],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_294":326,"_327":326,"_328":329,"_330":331},"NSPECT-KZF6-2OL5","value","filter",{"_332":331,"_327":326},"category","label","field",{"_194":1313,"_196":1314},"ngcApps",[1266],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1247":1248,"_1249":1250,"_1251":1252,"_1253":1254,"_1255":344,"_225":368,"_1256":1257,"_1258":56,"_1259":173,"_1260":173,"_1261":1262,"_1263":173,"_1264":1265},"amplitude",{"_1244":1245,"_880":-7,"_1246":31},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1238":1239,"_1240":1241,"_1242":1243},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.10.0","defaultSearchSuggestions",{"_1120":448,"_1121":1122,"_1123":1124,"_1125":1126},"announcements",[],"consentState",{"_406":1110,"_1111":173,"_1112":31,"_1113":31,"_1114":1115},"consentConfig",{"_388":389,"_390":391,"_392":393,"_394":395,"_396":397,"_398":399,"_400":401,"_402":403,"_404":405,"_406":407},"showContactAdmin",1784811586142,[],[],[382,383,384,385,386,387],{"_45":60,"_47":71,"_49":189,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":69,"_49":189,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":67,"_49":189,"_51":-5,"_53":-5,"_55":56},{"_45":60,"_47":61,"_49":189,"_51":-5,"_53":-5,"_55":56},{"_45":57,"_47":58,"_49":189,"_51":-5,"_53":-5,"_55":56},{"_45":46,"_47":48,"_49":189,"_51":-5,"_53":-5,"_55":56},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566],[429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445],{"_446":540,"_294":540,"_253":448,"_419":541},{"_446":536,"_294":536,"_253":448,"_419":537},{"_446":531,"_294":531,"_253":448,"_419":532},{"_446":527,"_294":527,"_253":448,"_419":528},{"_446":522,"_294":522,"_253":448,"_419":523},{"_446":517,"_294":517,"_253":448,"_419":518},{"_446":509,"_294":509,"_253":448,"_419":510},{"_446":501,"_294":501,"_253":448,"_419":502},{"_446":496,"_294":496,"_253":448,"_419":497},{"_446":491,"_294":491,"_253":448,"_419":492},{"_446":486,"_294":486,"_253":448,"_419":487},{"_446":481,"_294":481,"_253":448,"_419":482},{"_446":476,"_294":476,"_253":448,"_419":477},{"_446":469,"_294":469,"_253":448,"_419":470},{"_446":464,"_294":464,"_253":448,"_419":465},{"_446":457,"_294":457,"_253":448,"_419":458},{"_446":447,"_294":447,"_253":448,"_419":449},"hostName","amazon-adsystem.com","",[450,451],{"_77":456,"_453":447,"_454":31,"_253":455},{"_77":452,"_453":447,"_454":31,"_253":455},"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",[459,460],{"_77":463,"_453":457,"_454":31,"_253":462},{"_77":461,"_453":457,"_454":31,"_253":462},"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. 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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.",[637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661],[608,609,610,611,612],{"_446":522,"_294":522,"_253":448,"_419":632},{"_446":496,"_294":496,"_253":448,"_419":627},{"_446":622,"_294":622,"_253":448,"_419":623},{"_446":617,"_294":617,"_253":448,"_419":618},{"_446":613,"_294":613,"_253":448,"_419":614},"nvidia.partners",[615],{"_77":616,"_453":613,"_454":31,"_253":490},"edge","app.hushly.com",[619],{"_77":620,"_453":617,"_454":31,"_253":621},"*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",[624],{"_77":625,"_453":622,"_454":31,"_253":626},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748],[700,701,702,703],{"_446":715,"_294":715,"_253":448,"_419":716},{"_446":710,"_294":710,"_253":448,"_419":711},{"_446":509,"_294":509,"_253":448,"_419":707},{"_446":622,"_294":622,"_253":448,"_419":704},[705],{"_77":706,"_453":622,"_454":173,"_253":626},"_gh_sess",[708],{"_77":709,"_453":509,"_454":173,"_253":516},"YSC","bizible.com",[712],{"_77":713,"_453":710,"_454":31,"_253":714},"_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",[717],{"_77":718,"_453":715,"_454":31,"_253":719},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_77":790,"_453":568,"_454":31,"_253":791},{"_77":775,"_453":789,"_454":173,"_253":776},{"_77":758,"_453":571,"_454":31,"_253":683},{"_77":758,"_453":671,"_454":31,"_253":683},{"_77":787,"_453":568,"_454":31,"_253":788},{"_77":775,"_453":574,"_454":173,"_253":776},{"_77":785,"_453":568,"_454":31,"_253":786},{"_77":775,"_453":571,"_454":173,"_253":776},{"_77":783,"_453":670,"_454":31,"_253":784},{"_77":775,"_453":692,"_454":173,"_253":776},{"_77":755,"_453":571,"_454":31,"_253":757},{"_77":781,"_453":568,"_454":173,"_253":782},{"_77":779,"_453":568,"_454":31,"_253":780},{"_77":777,"_453":568,"_454":173,"_253":778},{"_77":775,"_453":666,"_454":173,"_253":776},{"_77":773,"_453":568,"_454":31,"_253":774},{"_77":771,"_453":568,"_454":31,"_253":772},{"_77":769,"_453":568,"_454":173,"_253":770},{"_77":767,"_453":675,"_454":31,"_253":768},{"_77":755,"_453":666,"_454":31,"_253":757},{"_77":765,"_453":568,"_454":31,"_253":766},{"_77":763,"_453":568,"_454":173,"_253":764},{"_77":761,"_453":568,"_454":31,"_253":762},{"_77":759,"_453":568,"_454":31,"_253":760},{"_77":758,"_453":568,"_454":31,"_253":683},{"_77":755,"_453":756,"_454":31,"_253":757},{"_77":753,"_453":568,"_454":31,"_253":754},{"_77":751,"_453":568,"_454":173,"_253":752},{"_77":749,"_453":568,"_454":31,"_253":750},"_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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Name	Size	Updated	Actions