Gemma 4 31B IT

Description

Gemma 4 31B IT is an open multimodal model built by Google DeepMind that handles text and image inputs, can process video as sequences of frames, and generates text output. It is designed to deliver frontier-level performance for reasoning, agentic workflows, coding, and multimodal understanding on consumer GPUs and workstations, with a 256K-token context window and support for over 140 languages. The model uses a hybrid attention mechanism that interleaves local sliding-window and full global attention, with unified Keys and Values in global layers and Proportional RoPE (p-RoPE) to support long-context performance.

This model is ready for commercial/non-commercial use.

Third-Party Community Consideration:

This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA Gemma 4 31B IT Model Card

License and Terms of Use:

GOVERNING TERMS: Use of this model is governed by the NVIDIA Open Model License Agreement. Additional Information:Apache License, Version 2.0.

Deployment Geography:

Global

Use Case:

Use Case: Designed for text generation, chatbots and conversational AI, text summarization, image data extraction, reasoning, coding, multimodal understanding, function calling, and research or educational use.

Release Date:

Build.NVIDIA.com: 04/02/2026 via link
\nHuggingface: 04/02/2026 via link

Model Architecture:

Architecture Type: Transformer
\nNetwork Architecture: Gemma 4
\nTotal Parameters: 30.7B
\nVocabulary Size: 262,144

Input:

Input Types: Text, Image, Video
\nInput Formats: String, Red, Green, Blue (RGB), Video (MP4/WebM)
\nInput Parameters: One-Dimensional (1D), Two-Dimensional (2D), Three-Dimensional (3D)
\nOther Input Properties: Supports variable image aspect ratios and resolutions, configurable visual token budgets of 70, 140, 280, 560, and 1120, and video inputs up to 60 seconds at one frame per second.
\nInput Context Length (ISL): 256K

Output:

Output Types: Text
\nOutput Format: String
\nOutput Parameters: One-Dimensional (1D)
\nOther Output Properties: Generates text responses for chat, reasoning, coding, multimodal understanding, and function-calling workflows.

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 Engines:

vLLM
SGLang

Supported Hardware:

NVIDIA Ampere: A100
NVIDIA Blackwell: B100, B200, GB200
NVIDIA Hopper: H100, H200

Preferred Operating Systems: Linux

The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.

Model Version(s)

Gemma 4 31B IT v1.0

Training, Testing, and Evaluation Datasets:

Training Dataset

Data Modality: Text, Image, Audio, Other (Code)
\nTraining Data Collection: Automated
\nTraining Labeling: Undisclosed
\nTraining Properties: Large-scale multimodal pre-training data spanning web documents, code, images, and audio, with a cutoff date of January 2025 and coverage in over 140 languages. Data was filtered for CSAM, sensitive data, quality, and safety.

Testing Dataset

Testing Data Collection: Undisclosed
\nTesting Labeling: Undisclosed
\nTesting Properties: Undisclosed

Evaluation Dataset

Evaluation Benchmark Score: Benchmark results are provided in the Gemma 4 31B IT Model Card comparing Gemma 4 31B IT to similar sized models across text and vision tasks. Specific scores vary by benchmark.
\nEvaluation Data Collection: Hybrid: Automated, Human
\nEvaluation Labeling: Hybrid: Automated, Human
\nEvaluation Properties: Gemma 4 models were evaluated across reasoning, coding, vision, long-context, and safety tasks, with benchmark table results reported for instruction-tuned models. Evaluation Methodology Notes:

Tau2 is reported as an average over 3 runs.
HLE is reported with and without search.
OmniDocBench 1.5 uses average edit distance, where lower is better.
MRCR v2 8 needle 128k is reported as an average.
Safety evaluations included both automated and human assessments, and all safety testing was conducted without safety filters for both text-to-text and image-to-text settings.

Detailed Benchmark Comparison Table

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

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

Benchmark	Gemma 4 31B	Gemma 4 26B A4B	Gemma 4 E4B	Gemma 4 E2B	Gemma 3 27B (no think)
MMLU Pro	85.2%	82.6%	69.4%	60.0%	67.6%
AIME 2026 no tools	89.2%	88.3%	42.5%	37.5%	20.8%
LiveCodeBench v6	80.0%	77.1%	52.0%	44.0%	29.1%
Codeforces ELO	2150	1718	940	633	110
GPQA Diamond	84.3%	82.3%	58.6%	43.4%	42.4%
Tau2 (average over 3)	76.9%	68.2%	42.2%	24.5%	16.2%
HLE no tools	19.5%	8.7%	-	-	-
HLE with search	26.5%	17.2%	-	-	-
BigBench Extra Hard	74.4%	64.8%	33.1%	21.9%	19.3%
MMMLU	88.4%	86.3%	76.6%	67.4%	70.7%
Vision
MMMU Pro	76.9%	73.8%	52.6%	44.2%	49.7%
OmniDocBench 1.5 (average edit distance, lower is better)	0.131	0.149	0.181	0.290	0.365
MATH-Vision	85.6%	82.4%	59.5%	52.4%	46.0%
MedXPertQA MM	61.3%	58.1%	28.7%	23.5%	-
Audio
CoVoST	-	-	35.54	33.47	-
FLEURS (lower is better)	-	-	0.08	0.09	-
Long Context
MRCR v2 8 needle 128k (average)	66.4%	44.1%	25.4%	19.1%	13.5%

Inference

Acceleration Engine: vLLM
\nTest Hardware: NVIDIA Hopper H100

Additional Details

Best Practices

For best performance, use the partner-recommended sampling configuration of temperature=1.0, top_p=0.95, and top_k=64 across use cases.

Thinking Mode Configuration

Compared to Gemma 3, Gemma 4 uses standard system, assistant, and user roles. Thinking is enabled by including the <|think|> token at the start of the system prompt and disabled by removing it. When thinking is enabled, the model outputs its internal reasoning followed by the final answer using the structure <|channel>thought\\n[Internal reasoning]<channel|>. For models other than E2B and E4B, including 31B, disabling thinking still produces the tags with an empty thought block before the final answer: <|channel>thought\\n<channel|>[Final answer]. Libraries such as Transformers and llama.cpp may handle chat-template details automatically.

Multi-Turn Conversations

In multi-turn conversations, historical model output should include only the final response. Thought content from previous turns should not be included before the next user turn begins.

Multimodal Input Guidance

For optimal multimodal performance, place image content before text in the prompt. For variable image resolution, Gemma 4 uses a configurable visual token budget with supported values of 70, 140, 280, 560, and 1120. Lower budgets are better suited to classification, captioning, or video understanding where faster inference and processing many frames are preferred, while higher budgets are better suited to OCR, document parsing, or reading small text.

Video Length

The 31B model can process video as frames and supports video inputs up to 60 seconds when frames are processed at one frame per second.

Usage and Limitations

These models have certain limitations that users should be aware of.

Intended Usage

The Gemma 4 model card describes a broad intended-usage set for the Gemma 4 family across content creation, communication, research, and education. Example uses include text generation for creative and practical formats such as poems, scripts, code, marketing copy, and email drafts; chatbots and conversational AI for customer service, virtual assistants, or interactive applications; text summarization for corpora, research papers, or reports; image data extraction for interpreting and summarizing visual data into text communications; natural language processing and VLM research; language learning tools; and knowledge exploration over large bodies of text. It also notes audio processing and interaction for the smaller E2B and E4B models only.

Limitations

Training data quality and diversity significantly influence model capabilities, and biases or gaps in the training data can lead to limitations in model responses.
The scope of the training dataset affects which subject areas the model can handle effectively.
Models perform best on tasks framed with clear prompts and instructions; open-ended or highly complex tasks may be challenging.
Performance can be influenced by the amount of context provided, with longer context often helping only up to a point.
Natural language ambiguity and nuance, including sarcasm and figurative language, may still be difficult for the model to handle reliably.
The model generates responses from learned training patterns rather than from a live knowledge base, so outputs can contain incorrect or outdated factual statements.
The model may lack the ability to apply common-sense reasoning in some situations.

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.

Please make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included.

Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

Get Help

NVIDIA NIM Documentation

Visit the VLM NIM page for release documentation, deployment guides and more.

NVIDIA Developer Community Forum

Get access to community knowledge base articles and support cases (https://forums.developer.nvidia.com/)

Enterprise Support

Get access to knowledge base articles and support cases or submit a ticket.

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It is designed to deliver frontier-level performance for reasoning, agentic workflows, coding, and multimodal understanding on consumer GPUs and workstations, with a 256K-token context window and support for over 140 languages. The model uses a hybrid attention mechanism that interleaves local sliding-window and full global attention, with unified Keys and Values in global layers and Proportional RoPE (p-RoPE) to support long-context performance.\n\n*This model is ready for commercial/non-commercial use.*\n\n## Third-Party Community Consideration:\nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA [Gemma 4 31B IT Model Card](https://huggingface.co/google/gemma-4-31B-it)\n\n## License and Terms of Use:\n**GOVERNING TERMS:** Use of this model is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). Additional Information:[Apache License, Version 2.0](https://ai.google.dev/gemma/docs/gemma_4_license).\n\n## Deployment Geography:\nGlobal\n\n## Use Case:\n**Use Case:** Designed for text generation, chatbots and conversational AI, text summarization, image data extraction, reasoning, coding, multimodal understanding, function calling, and research or educational use.\n\n## Release Date:\n**Build.NVIDIA.com:** 04/02/2026 via [link](https://build.nvidia.com/google/gemma-4-31b-it) \n**Huggingface:** 04/02/2026 via [link](https://huggingface.co/google/gemma-4-31B-it)\n\n\n## Model Architecture:\n**Architecture Type:** Transformer \n**Network Architecture:** Gemma 4 \n**Total Parameters:** 30.7B \n**Vocabulary Size:** 262,144\n\n### Input:\n**Input Types:** Text, Image, Video \n**Input Formats:** String, Red, Green, Blue (RGB), Video (MP4/WebM) \n**Input Parameters:** One-Dimensional (1D), Two-Dimensional (2D), Three-Dimensional (3D) \n**Other Input Properties:** Supports variable image aspect ratios and resolutions, configurable visual token budgets of 70, 140, 280, 560, and 1120, and video inputs up to 60 seconds at one frame per second. \n**Input Context Length (ISL):** 256K\n\n### Output:\n**Output Types:** Text \n**Output Format:** String \n**Output Parameters:** One-Dimensional (1D) \n**Other Output Properties:** Generates text responses for chat, reasoning, coding, multimodal understanding, and function-calling workflows.\n\n__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.__\n\n## Software Integration:\n**Runtime Engines:**\n- **vLLM**\n- **SGLang**\n\n**Supported Hardware:**\n- **NVIDIA Ampere:** A100\n- **NVIDIA Blackwell:** B100, B200, GB200\n- **NVIDIA Hopper:** H100, H200\n\n**Preferred Operating Systems:** Linux\n\n__The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.__\n\n## Model Version(s)\nGemma 4 31B IT v1.0\n\n## Training, Testing, and Evaluation Datasets:\n\n### Training Dataset\n**Data Modality:** Text, Image, Audio, Other (Code) \n**Training Data Collection:** Automated \n**Training Labeling:** Undisclosed \n**Training Properties:** Large-scale multimodal pre-training data spanning web documents, code, images, and audio, with a cutoff date of January 2025 and coverage in over 140 languages. Data was filtered for CSAM, sensitive data, quality, and safety.\n\n### Testing Dataset\n**Testing Data Collection:** Undisclosed \n**Testing Labeling:** Undisclosed \n**Testing Properties:** Undisclosed\n\n### Evaluation Dataset\n**Evaluation Benchmark Score:** Benchmark results are provided in the [Gemma 4 31B IT Model Card](https://huggingface.co/google/gemma-4-31B-it) comparing Gemma 4 31B IT to similar sized models across text and vision tasks. Specific scores vary by benchmark. \n**Evaluation Data Collection:** Hybrid: Automated, Human \n**Evaluation Labeling:** Hybrid: Automated, Human \n**Evaluation Properties:** Gemma 4 models were evaluated across reasoning, coding, vision, long-context, and safety tasks, with benchmark table results reported for instruction-tuned models. Evaluation Methodology Notes:\n- `Tau2` is reported as an average over 3 runs.\n- `HLE` is reported with and without search.\n- `OmniDocBench 1.5` uses average edit distance, where lower is better.\n- `MRCR v2 8 needle 128k` is reported as an average.\n- Safety evaluations included both automated and human assessments, and all safety testing was conducted without safety filters for both text-to-text and image-to-text settings.\n\n

Detailed Benchmark Comparison Table

\n\n| Benchmark | Gemma 4 31B | Gemma 4 26B A4B | Gemma 4 E4B | Gemma 4 E2B | Gemma 3 27B (no think) |\n|-----------|-------------|-----------------|-------------|-------------|------------------------|\n| MMLU Pro | 85.2% | 82.6% | 69.4% | 60.0% | 67.6% |\n| AIME 2026 no tools | 89.2% | 88.3% | 42.5% | 37.5% | 20.8% |\n| LiveCodeBench v6 | 80.0% | 77.1% | 52.0% | 44.0% | 29.1% |\n| Codeforces ELO | 2150 | 1718 | 940 | 633 | 110 |\n| GPQA Diamond | 84.3% | 82.3% | 58.6% | 43.4% | 42.4% |\n| Tau2 (average over 3) | 76.9% | 68.2% | 42.2% | 24.5% | 16.2% |\n| HLE no tools | 19.5% | 8.7% | - | - | - |\n| HLE with search | 26.5% | 17.2% | - | - | - |\n| BigBench Extra Hard | 74.4% | 64.8% | 33.1% | 21.9% | 19.3% |\n| MMMLU | 88.4% | 86.3% | 76.6% | 67.4% | 70.7% |\n| **Vision** | | | | | |\n| MMMU Pro | 76.9% | 73.8% | 52.6% | 44.2% | 49.7% |\n| OmniDocBench 1.5 (average edit distance, lower is better) | 0.131 | 0.149 | 0.181 | 0.290 | 0.365 |\n| MATH-Vision | 85.6% | 82.4% | 59.5% | 52.4% | 46.0% |\n| MedXPertQA MM | 61.3% | 58.1% | 28.7% | 23.5% | - |\n| **Audio** | | | | | |\n| CoVoST | - | - | 35.54 | 33.47 | - |\n| FLEURS (lower is better) | - | - | 0.08 | 0.09 | - |\n| **Long Context** | | | | | |\n| MRCR v2 8 needle 128k (average) | 66.4% | 44.1% | 25.4% | 19.1% | 13.5% |\n\n

\n\n## Inference\n**Acceleration Engine:** vLLM \n**Test Hardware:** NVIDIA Hopper H100\n\n## Additional Details\n### Best Practices\nFor best performance, use the partner-recommended sampling configuration of `temperature=1.0`, `top_p=0.95`, and `top_k=64` across use cases.\n\n### Thinking Mode Configuration\nCompared to Gemma 3, Gemma 4 uses standard system, assistant, and user roles. Thinking is enabled by including the `<|think|>` token at the start of the system prompt and disabled by removing it. When thinking is enabled, the model outputs its internal reasoning followed by the final answer using the structure `<|channel>thought\\n[Internal reasoning]`. For models other than `E2B` and `E4B`, including `31B`, disabling thinking still produces the tags with an empty thought block before the final answer: `<|channel>thought\\n[Final answer]`. Libraries such as Transformers and `llama.cpp` may handle chat-template details automatically.\n\n### Multi-Turn Conversations\nIn multi-turn conversations, historical model output should include only the final response. Thought content from previous turns should not be included before the next user turn begins.\n\n### Multimodal Input Guidance\nFor optimal multimodal performance, place image content before text in the prompt. For variable image resolution, Gemma 4 uses a configurable visual token budget with supported values of `70`, `140`, `280`, `560`, and `1120`. Lower budgets are better suited to classification, captioning, or video understanding where faster inference and processing many frames are preferred, while higher budgets are better suited to OCR, document parsing, or reading small text.\n\n### Video Length\nThe `31B` model can process video as frames and supports video inputs up to `60` seconds when frames are processed at one frame per second.\n\n### Usage and Limitations\nThese models have certain limitations that users should be aware of.\n\n#### Intended Usage\nThe Gemma 4 model card describes a broad intended-usage set for the Gemma 4 family across content creation, communication, research, and education. Example uses include text generation for creative and practical formats such as poems, scripts, code, marketing copy, and email drafts; chatbots and conversational AI for customer service, virtual assistants, or interactive applications; text summarization for corpora, research papers, or reports; image data extraction for interpreting and summarizing visual data into text communications; natural language processing and VLM research; language learning tools; and knowledge exploration over large bodies of text. It also notes audio processing and interaction for the smaller `E2B` and `E4B` models only.\n\n#### Limitations\n- Training data quality and diversity significantly influence model capabilities, and biases or gaps in the training data can lead to limitations in model responses.\n- The scope of the training dataset affects which subject areas the model can handle effectively.\n- Models perform best on tasks framed with clear prompts and instructions; open-ended or highly complex tasks may be challenging.\n- Performance can be influenced by the amount of context provided, with longer context often helping only up to a point.\n- Natural language ambiguity and nuance, including sarcasm and figurative language, may still be difficult for the model to handle reliably.\n- The model generates responses from learned training patterns rather than from a live knowledge base, so outputs can contain incorrect or outdated factual statements.\n- The model may lack the ability to apply common-sense reasoning in some situations.\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\nPlease make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included.\n\nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
\n\n## Get Help\n\n### NVIDIA NIM Documentation\nVisit the [VLM NIM page](https://docs.nvidia.com/nim/vision-language-models/latest/introduction.html) for release documentation, deployment guides and more.\n\n## NVIDIA Developer Community Forum\nGet access to community knowledge base articles and support cases (https://forums.developer.nvidia.com/)
\n\n\n## Enterprise Support\nGet access to knowledge base articles and support cases or [submit a ticket](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).","displayName","Gemma 4 31B IT",["D",1787772146901],"publisher","Google","latestVersion","versions","collectionCounts","imageUrl","/api/image/nim/google/models/gemma-4-31b-it","rawImageUrl","canGuestDownload","latestVersionSizeInBytes",23297595143,"licenseTerms",{"_291":292,"_293":294,"_295":296,"_297":30},"isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[282,283],"displayLabels",[274],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_241":275,"_276":275,"_277":278,"_279":280},"NSPECT-LFXW-A6M4","value","filter",{"_281":280,"_276":275},"category","label","field",{"_241":289,"_276":289,"_277":290,"_279":287},{"_241":284,"_276":285,"_277":286,"_279":287},"NVIDIA NIM","nimmcro_nvidia_nim",{"_281":288,"_276":285},"feature","nvidia_nim","NVIDIA AI Enterprise Supported",{"_281":280,"_276":289},"governingTerms","

The NIM container is governed by theNVIDIA Software License AgreementandProduct-Specific Terms for NVIDIA AI Products;Use of this model is governed by theNVIDIA Open Model License Agreement. Additional Information:Apache License, Version 2.0.

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For more information about our privacy practices, please see our Privacy Policy.","mainText","Cookie Settings","alertNoticeText",{"_1069":1070,"_1071":1072,"_1073":1074,"_1075":1076},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[374,375,376,377,378],{"_379":759,"_77":760,"_165":761,"_383":85,"_384":16,"_386":762,"_388":763},{"_379":662,"_77":663,"_165":664,"_383":30,"_384":53,"_386":665,"_388":666},{"_379":569,"_77":570,"_165":571,"_383":30,"_384":572,"_386":573,"_388":574},{"_379":390,"_77":391,"_165":392,"_383":30,"_384":393,"_386":394,"_388":395},{"_379":380,"_77":381,"_165":382,"_383":30,"_384":385,"_386":387,"_388":389},"optanonGroupId","C0005","Social Media Cookies","These cookies are set by a range of social media services that we have added to the site to enable you to share our content with your friends and networks. 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,[511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532],[396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412],{"_413":506,"_241":506,"_165":166,"_386":507},{"_413":502,"_241":502,"_165":166,"_386":503},{"_413":497,"_241":497,"_165":166,"_386":498},{"_413":493,"_241":493,"_165":166,"_386":494},{"_413":488,"_241":488,"_165":166,"_386":489},{"_413":483,"_241":483,"_165":166,"_386":484},{"_413":475,"_241":475,"_165":166,"_386":476},{"_413":467,"_241":467,"_165":166,"_386":468},{"_413":462,"_241":462,"_165":166,"_386":463},{"_413":457,"_241":457,"_165":166,"_386":458},{"_413":452,"_241":452,"_165":166,"_386":453},{"_413":447,"_241":447,"_165":166,"_386":448},{"_413":442,"_241":442,"_165":166,"_386":443},{"_413":435,"_241":435,"_165":166,"_386":436},{"_413":430,"_241":430,"_165":166,"_386":431},{"_413":423,"_241":423,"_165":166,"_386":424},{"_413":414,"_241":414,"_165":166,"_386":415},"hostName","amazon-adsystem.com",[416,417],{"_77":422,"_419":414,"_420":30,"_165":421},{"_77":418,"_419":414,"_420":30,"_165":421},"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",[425,426],{"_77":429,"_419":423,"_420":30,"_165":428},{"_77":427,"_419":423,"_420":30,"_165":428},"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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If you do not allow these cookies, then some or all of these services may not function properly.",4,[604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628],[575,576,577,578,579],{"_413":488,"_241":488,"_165":166,"_386":599},{"_413":462,"_241":462,"_165":166,"_386":594},{"_413":589,"_241":589,"_165":166,"_386":590},{"_413":584,"_241":584,"_165":166,"_386":585},{"_413":580,"_241":580,"_165":166,"_386":581},"nvidia.partners",[582],{"_77":583,"_419":580,"_420":30,"_165":456},"edge","app.hushly.com",[586],{"_77":587,"_419":584,"_420":30,"_165":588},"*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",[591],{"_77":592,"_419":589,"_420":30,"_165":593},"_octo","This domain is owned by Github. 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users logged in across NVIDIA domains.","api.ngc.nvidia.com","This cookie is managed by AWS and is used for load balancing.","bm_so","This cookie name is used to load the site as quickly as possible and to recognize bots using Akamai.","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","nv_co","__uxq412__ses.88c1","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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