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Use of this model is governed by theNVIDIA Open Model License Agreement. Additional Information:Apache 2.0 license.

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Description:

A multimodal Mixture of Experts (MoE) model containing 35B total parameters (3B activated), 40 layers, a context length of 262,144 tokens (extendable to 1,010,000 via YaRN), designed for agentic coding, multimodal reasoning, and long-context understanding across text, image, and video inputs. Key capabilities include thinking preservation across conversation turns, multi-token prediction, and tool calling for multi-step agent pipelines.\nQwen3.6-35B-A3B was developed by Qwen as a part of Qwen.

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 Qwen Model Card.

License and Terms of Use:

GOVERNING TERMS: The NIM container is governed by the NVIDIA Software License Agreement and the Product-Specific Terms for NVIDIA AI Products; and the use of the model is governed by the NVIDIA Open Model License Agreement.

Additional Information: Apache-2.0 License.

Deployment Geography:

Global

Use Case:

Developers building AI agents, coding assistants, and multimodal applications that process text, images, and video, requiring advanced reasoning, agentic coding, and long-context understanding.

Get Help

NVIDIA Developer Community Forum

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

Release Date:

NGC: 04/16/2026 via Qwen3.6-35B-A3B on NGC\nHuggingFace: 04/15/2026 via Qwen3.6-35B-A3B on HuggingFace

Reference(s):

Model Architecture:

Architecture Type: Transformer\nNetwork Architecture: Mixture of Experts (MoE) with 256 experts (8 routed + 1 shared per token)\nThis model was developed based on Qwen/Qwen3.6-35B-A3B.\nNumber of model parameters: 35B

Input:

Input Type(s): Text, Image, Video\nInput Format(s): Text: String; Image: Red, Green, Blue (RGB); Video: mp4, mov, webm\nInput Parameters: One-Dimensional (1D), Two Dimensional (2D), Three Dimensional (3D)\nOther Properties Related to Input: The model supports up to 262,144 tokens of context length (extensible to 1,010,000 via YaRN) and can operate in thinking or non-thinking modes via API flags. It accepts multimodal inputs (text, image, video) through the OpenAI-compatible chat endpoint.

Output:

Output Type(s): Text\nOutput Format: String\nOutput Parameters: One-Dimensional (1D)\nOther Properties Related to Output: The model can preserve thinking traces across messages and supports configurable sampling parameters (temperature, top_p, top_k, etc.). Output length can be set up to 81,920 tokens.

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): SGLang\nSupported Hardware Microarchitecture Compatibility:

NVIDIA Blackwell
NVIDIA Hopper

Supported Operating System(s): 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.

This AI model can be embedded as an Application Programming Interface (API) call into the software environment described above.

Model Version(s):

Qwen3.6-35B-A3B v3.6

Training, Testing, and Evaluation Datasets:

Training Dataset:

Data Modality:

Text
Image
Video\nImage Training Data Size: Undisclosed\nText Training Data Size: Undisclosed\nVideo Training Data Size: Undisclosed\nData Collection Method by dataset: Undisclosed\nLabeling Method by dataset: Undisclosed

Testing Dataset:

Data Collection Method by dataset: Undisclosed\nLabeling Method by dataset: Undisclosed\nProperties: Undisclosed

Evaluation Dataset:

Benchmark Results

Language Benchmarks

\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	Qwen3.5-27B	Gemma4-31B	Qwen3.5-35BA3B	Gemma4-26BA4B	Qwen3.6-35BA3B
SWE-bench Verified	75.0	52.0	70.0	17.4	73.4
SWE-bench Multilingual	69.3	51.7	60.3	17.3	67.2
SWE-bench Pro	51.2	35.7	44.6	13.8	49.5
Terminal-Bench 2.0	41.6	42.9	40.5	34.2	51.5
MMLU-Pro	86.1	85.2	85.3	82.6	85.2
GPQA Diamond	85.5	84.3	84.2	82.3	86.0
HLE	24.3	19.5	22.4	8.7	21.4
LiveCodeBench v6	80.7	80.0	74.6	77.1	80.4
AIME 2026	92.6	89.2	91.0	88.3	92.7
C-Eval	90.5	82.6	90.2	82.5	90.0

Vision Benchmarks

\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	Qwen3.5-27B	Claude-Sonnet-4.5	Gemma4-31B	Gemma4-26BA4B	Qwen3.5-35BA3B	Qwen3.6-35BA3B
MMMU	82.3	79.6	80.4	78.4	81.4	81.7
MMMU-Pro	75.0	68.4	76.9*	73.8*	75.1	75.3
Mathvista (mini)	87.8	79.8	79.3	79.4	86.2	86.4
RealWorldQA	83.7	70.3	72.3	72.2	84.1	85.3
MMBench EN-DEV-v1.1	92.6	88.3	90.9	89.0	91.5	92.8
OmniDocBench1.5	88.9	85.8	80.1	74.4	89.3	89.9
VideoMMMU	82.3	77.6	81.6	76.0	80.4	83.7
VideoMME (w sub.)	87.0	81.1	--	--	86.6	86.6
VideoMME (w/o sub.)	82.8	75.3	--	--	82.5	82.5
RefCOCO (avg)	90.9	--	--	--	89.2	92.0
ODInW13	41.1	--	--	--	42.6	50.8

Data Collection Method by dataset: [Hybrid: Automated, Human]\nLabeling Method by dataset: [Hybrid: Automated, Human]\nProperties (Quantity, Dataset Descriptions, Sensor(s)): The model is evaluated on a broad suite of benchmarks covering agent capabilities, vision-language tasks, general VQA, STEM and reasoning, knowledge, and spatial intelligence. These benchmarks include both human-curated problems (e.g., AIME, HMMT, HumanEval) and automatically generated or web-crawled datasets (e.g., Terminal-Bench, SkillsBench).

Inference:

Acceleration Engine: SGLang\nTest Hardware:

NVIDIA B200
NVIDIA H100
NVIDIA H200

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.

Users are responsible for model inputs and outputs. Users are responsible for ensuring safe integration of this model, including implementing guardrails as well as other safety mechanisms, prior to deployment.

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

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Key capabilities include thinking preservation across conversation turns, multi-token prediction, and tool calling for multi-step agent pipelines.\nQwen3.6-35B-A3B was developed by Qwen as a part of Qwen.\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 [Qwen Model Card](https://huggingface.co/Qwen/Qwen3.6-35B-A3B).\n\n## License and Terms of Use:\n**GOVERNING TERMS:** The NIM container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and the [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/); and the use of the model is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).\n\nAdditional Information: [Apache-2.0 License](https://huggingface.co/Qwen/Qwen3.6-35B-A3B).\n\n### Deployment Geography:\nGlobal\n\n### Use Case:\nDevelopers building AI agents, coding assistants, and multimodal applications that process text, images, and video, requiring advanced reasoning, agentic coding, and long-context understanding.\n\n## Get Help\n\n### NVIDIA Developer Community Forum\nGet access to community knowledge base articles and support cases (https://forums.developer.nvidia.com/)\n\n\n### Release Date:\n**NGC:** 04/16/2026 via [Qwen3.6-35B-A3B on NGC](https://catalog.ngc.nvidia.com/orgs/nim/teams/qwen/models/qwen36-35b-a3b)\n**HuggingFace:** 04/15/2026 via [Qwen3.6-35B-A3B on HuggingFace](https://huggingface.co/Qwen/Qwen3.6-35B-A3B)\n\n## Reference(s):\n* [Qwen3.6-35B-A3B: Agentic Coding Power, Now Open to All](https://qwen.ai/blog?id=qwen3.6-35b-a3b)\n* [vLLM PR 34330](https://github.com/vllm-project/vllm/pull/34330)\n* [SGLang PR 18467](https://github.com/sgl-project/sglang/pull/18467)\n* [HuggingFace model repository for Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B)\n\n## Model Architecture:\n**Architecture Type:** Transformer\n**Network Architecture:** Mixture of Experts (MoE) with 256 experts (8 routed + 1 shared per token)\n**This model was developed based on Qwen/Qwen3.6-35B-A3B.**\n**Number of model parameters:** 35B\n\n## Input:\n**Input Type(s):** Text, Image, Video\n**Input Format(s):** Text: String; Image: Red, Green, Blue (RGB); Video: mp4, mov, webm\n**Input Parameters:** One-Dimensional (1D), Two Dimensional (2D), Three Dimensional (3D)\n**Other Properties Related to Input:** The model supports up to 262,144 tokens of context length (extensible to 1,010,000 via YaRN) and can operate in thinking or non-thinking modes via API flags. It accepts multimodal inputs (text, image, video) through the OpenAI-compatible chat endpoint.\n\n## Output:\n**Output Type(s):** Text\n**Output Format:** String\n**Output Parameters:** One-Dimensional (1D)\n**Other Properties Related to Output:** The model can preserve thinking traces across messages and supports configurable sampling parameters (temperature, top_p, top_k, etc.). Output length can be set up to 81,920 tokens.\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):** SGLang\n**Supported Hardware Microarchitecture Compatibility:**\n* NVIDIA Blackwell\n* NVIDIA Hopper\n\n**Supported Operating System(s):** Linux\n\nThe 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\nThis AI model can be embedded as an Application Programming Interface (API) call into the software environment described above.\n\n## Model Version(s):\nQwen3.6-35B-A3B v3.6\n\n## Training, Testing, and Evaluation Datasets:\n\n### Training Dataset:\n\n**Data Modality:**\n* Text\n* Image\n* Video\n**Image Training Data Size:** Undisclosed\n**Text Training Data Size:** Undisclosed\n**Video Training Data Size:** Undisclosed\n**Data Collection Method by dataset:** Undisclosed\n**Labeling Method by dataset:** Undisclosed\n\n### Testing Dataset:\n\n**Data Collection Method by dataset:** Undisclosed\n**Labeling Method by dataset:** Undisclosed\n**Properties:** Undisclosed\n\n### Evaluation Dataset:\n### Benchmark Results\n\n#### Language Benchmarks\n| Benchmark | Qwen3.5-27B | Gemma4-31B | Qwen3.5-35BA3B | Gemma4-26BA4B | Qwen3.6-35BA3B |\n|---|---|---|---|---|---|\n| SWE-bench Verified | 75.0 | 52.0 | 70.0 | 17.4 | 73.4 |\n| SWE-bench Multilingual | 69.3 | 51.7 | 60.3 | 17.3 | 67.2 |\n| SWE-bench Pro | 51.2 | 35.7 | 44.6 | 13.8 | 49.5 |\n| Terminal-Bench 2.0 | 41.6 | 42.9 | 40.5 | 34.2 | 51.5 |\n| MMLU-Pro | 86.1 | 85.2 | 85.3 | 82.6 | 85.2 |\n| GPQA Diamond | 85.5 | 84.3 | 84.2 | 82.3 | 86.0 |\n| HLE | 24.3 | 19.5 | 22.4 | 8.7 | 21.4 |\n| LiveCodeBench v6 | 80.7 | 80.0 | 74.6 | 77.1 | 80.4 |\n| AIME 2026 | 92.6 | 89.2 | 91.0 | 88.3 | 92.7 |\n| C-Eval | 90.5 | 82.6 | 90.2 | 82.5 | 90.0 |\n\n#### Vision Benchmarks\n| Benchmark | Qwen3.5-27B | Claude-Sonnet-4.5 | Gemma4-31B | Gemma4-26BA4B | Qwen3.5-35BA3B | Qwen3.6-35BA3B |\n|---|---|---|---|---|---|---|\n| MMMU | 82.3 | 79.6 | 80.4 | 78.4 | 81.4 | 81.7 |\n| MMMU-Pro | 75.0 | 68.4 | 76.9* | 73.8* | 75.1 | 75.3 |\n| Mathvista (mini) | 87.8 | 79.8 | 79.3 | 79.4 | 86.2 | 86.4 |\n| RealWorldQA | 83.7 | 70.3 | 72.3 | 72.2 | 84.1 | 85.3 |\n| MMBench EN-DEV-v1.1 | 92.6 | 88.3 | 90.9 | 89.0 | 91.5 | 92.8 |\n| OmniDocBench1.5 | 88.9 | 85.8 | 80.1 | 74.4 | 89.3 | 89.9 |\n| VideoMMMU | 82.3 | 77.6 | 81.6 | 76.0 | 80.4 | 83.7 |\n| VideoMME (w sub.) | 87.0 | 81.1 | -- | -- | 86.6 | 86.6 |\n| VideoMME (w/o sub.) | 82.8 | 75.3 | -- | -- | 82.5 | 82.5 |\n| RefCOCO (avg) | 90.9 | -- | -- | -- | 89.2 | 92.0 |\n| ODInW13 | 41.1 | -- | -- | -- | 42.6 | 50.8 |\n\n**Data Collection Method by dataset:** [Hybrid: Automated, Human]\n**Labeling Method by dataset:** [Hybrid: Automated, Human]\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** The model is evaluated on a broad suite of benchmarks covering agent capabilities, vision-language tasks, general VQA, STEM and reasoning, knowledge, and spatial intelligence. These benchmarks include both human-curated problems (e.g., AIME, HMMT, HumanEval) and automatically generated or web-crawled datasets (e.g., Terminal-Bench, SkillsBench).\n\n## Inference:\n**Acceleration Engine:** SGLang\n**Test Hardware:**\n* NVIDIA B200\n* NVIDIA H100\n* NVIDIA H200\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\nUsers are responsible for model inputs and outputs. Users are responsible for ensuring safe integration of this model, including implementing guardrails as well as other safety mechanisms, prior to deployment.\n\nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","Qwen3.6-35B-A3B","updatedDate",["D",1776460540698],"publisher","Qwen","latestVersion","nim-fp8","versions","collectionCounts","imageUrl","/api/image/nim/qwen/models/qwen3.6-35b-a3b","rawImageUrl","","canGuestDownload","latestVersionSizeInBytes",37493007786,"licenseTerms",{"_177":64,"_178":14,"_179":180,"_181":16},"isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[171],"displayLabels",[160,161],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_124":169,"_163":169,"_164":170,"_166":167},{"_124":162,"_163":162,"_164":165,"_166":167},"NSPECT-4L88-HB6N","value","filter",{"_168":167,"_163":162},"category","label","field","False",{"_168":167,"_163":169},{"_124":172,"_163":173,"_164":174,"_166":175},"NVIDIA NIM","nimmcro_nvidia_nim",{"_168":176,"_163":173},"feature","nvidia_nim","governingTerms","licenseId","licenseVersion","v1.0","needsAcceptance","appConfig",{"_1363":1364,"_216":1365},"ngcApps",[1316],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1297":1298,"_1299":1300,"_1301":1302,"_1303":1304,"_1305":194,"_81":221,"_1306":1307,"_1308":47,"_1309":68,"_1310":68,"_1311":1312,"_1313":68,"_1314":1315},"amplitude",{"_1294":1295,"_895":-7,"_1296":16},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1288":1289,"_1290":1291,"_1292":1293},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","https://api.ngc.nvidia.com","ngcHelmOrigin","https://helm.ngc.nvidia.com","origin","https://catalog.ngc.nvidia.com","user","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1170":137,"_1171":1172,"_1173":1174,"_1175":1176},"announcements",[],"consentState",{"_258":1160,"_1161":68,"_1162":16,"_1163":16,"_1164":1165},"consentConfig",{"_240":241,"_242":243,"_244":245,"_246":247,"_248":249,"_250":251,"_252":253,"_254":255,"_256":257,"_258":259},"showContactAdmin",1784265406337,[],[],[235,236,237,238,239],{"_36":51,"_38":61,"_40":57,"_42":-5,"_44":-5,"_46":47},{"_36":51,"_38":58,"_40":57,"_42":-5,"_44":-5,"_46":47},{"_36":51,"_38":52,"_40":57,"_42":-5,"_44":-5,"_46":47},{"_36":48,"_38":49,"_40":41,"_42":43,"_44":50,"_46":47},{"_36":37,"_38":39,"_40":57,"_42":-5,"_44":-5,"_46":47},"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,[410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444],[283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299],{"_300":406,"_124":406,"_122":137,"_273":407},{"_300":401,"_124":401,"_122":137,"_273":402},{"_300":397,"_124":397,"_122":137,"_273":398},{"_300":392,"_124":392,"_122":137,"_273":393},{"_300":387,"_124":387,"_122":137,"_273":388},{"_300":375,"_124":375,"_122":137,"_273":376},{"_300":363,"_124":363,"_122":137,"_273":364},{"_300":358,"_124":358,"_122":137,"_273":359},{"_300":347,"_124":347,"_122":137,"_273":348},{"_300":342,"_124":342,"_122":137,"_273":343},{"_300":337,"_124":337,"_122":137,"_273":338},{"_300":332,"_124":332,"_122":137,"_273":333},{"_300":327,"_124":327,"_122":137,"_273":328},{"_300":322,"_124":322,"_122":137,"_273":323},{"_300":317,"_124":317,"_122":137,"_273":318},{"_300":310,"_124":310,"_122":137,"_273":311},{"_300":301,"_124":301,"_122":137,"_273":302},"hostName","amazon-adsystem.com",[303,304],{"_267":309,"_306":301,"_307":16,"_122":308},{"_267":305,"_306":301,"_307":16,"_122":308},"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",[312,313],{"_267":316,"_306":310,"_307":16,"_122":315},{"_267":314,"_306":310,"_307":16,"_122":315},"IDE","This domain is owned by Doubleclick (Google). 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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,[533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553],[498,499,500,501,502],{"_300":392,"_124":392,"_122":137,"_273":524},{"_300":358,"_124":358,"_122":137,"_273":519},{"_300":512,"_124":512,"_122":137,"_273":513},{"_300":508,"_124":508,"_122":137,"_273":509},{"_300":503,"_124":503,"_122":137,"_273":504},"nvcrm.my.site.com",[505],{"_267":506,"_306":503,"_307":16,"_122":507},"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",[510],{"_267":511,"_306":508,"_307":16,"_122":341},"edge","github.com",[514,515],{"_267":518,"_306":512,"_307":16,"_122":517},{"_267":516,"_306":512,"_307":16,"_122":517},"logged_in","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.modal.cuda","This cookie name suggests it is related to a modal popup functionality on NVIDIA's website, possibly to remember user preferences or interactions with the modal.","readme_library","docs.api.nvidia.com","_im_ses.1006927","geo_locator","nv.modal.deepstream","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.","_im_id.1006927","_hjUserAttributesHash","docs.nvidia.com","This cookie name is associated with Hotjar which is used to know when any user attribute has changed and needs to be updated.","mbox","Cookie set by Adobe SiteCatalyst analytics software - used for measuring the performance of page content using A/B split testing","nv.modal.cudnn","This cookie name suggests it is related to modal popup handling or user interface preferences on NVIDIA's website, specifically for the cuDNN library section.","readme_language","nv.modal.hpc","This cookie name suggests it is related to a modal pop-up on a website, possibly to control the display of a high-priority cookie notice or other important information to the user.","at_check","Used by Adobe Target to check if cookies are enabled/supported on the browser","_biz_uid","This cookie is used to record website and server performance with each visit to the site. 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Information collected with these cookies is used to measure and find ways to improve website 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