Qwen3.6-35B-A3B

Description

A multimodal mixture-of-experts (MoE) model containing 35B total parameters (3B activated), 40 layers, and a context length of 262k tokens (extendable to 1.01M 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 the Qwen team.\nThis model is ready for commercial or 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: Use of this container is governed by the NVIDIA Software License Agreement and Product-Specific Terms for NVIDIA AI Products. Use of the model is governed by the NVIDIA Open Model Agreement. ADDITIONAL INFORMATION: Apache 2.0.

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 07/15/2026 via Qwen3.6-35B-A3B on NGC

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 262k tokens of context length (extensible to 1.01M 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. 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): vLLM\nSupported Hardware Microarchitecture Compatibility:

NVIDIA DGX Spark

Precision: FP8, NVFP4\nSupported Operating System(s): Ubuntu 22.04, Ubuntu 24.04

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\nProperties: 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, Manually-Collected]\nLabeling Method by dataset: [Hybrid: Automated, Manually-Labeled]\nProperties: The model is evaluated on benchmarks covering agent capabilities, vision-language tasks, general visual question answering, STEM and reasoning, knowledge, and spatial intelligence.

Inference

Acceleration Engine: vLLM\nPrecision: FP8, NVFP4\nTest Hardware:

NVIDIA DGX Spark

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. Developers should work with their internal developer team to ensure these software components meet requirements for the relevant industry and use case and address 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 the Qwen team.\nThis model is ready for commercial or 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:** Use of this container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/). Use of the model is governed by the [NVIDIA Open Model Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-agreement/). **ADDITIONAL INFORMATION:** [Apache 2.0](https://huggingface.co/Qwen/Qwen3.6-35B-A3B/blob/main/LICENSE).\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## Release Date\n**NGC** 07/15/2026 via [Qwen3.6-35B-A3B on NGC](https://catalog.ngc.nvidia.com/orgs/nim/teams/qwen/models/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* [Qwen3.6-35B-A3B Model Card](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 262k tokens of context length (extensible to 1.01M 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. 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):** vLLM\n**Supported Hardware Microarchitecture Compatibility:**\n* NVIDIA DGX Spark\n\n**Precision:** FP8, NVFP4\n**Supported Operating System(s):** Ubuntu 22.04, Ubuntu 24.04\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**Properties:** 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\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, Manually-Collected]\n**Labeling Method by dataset:** [Hybrid: Automated, Manually-Labeled]\n**Properties:** The model is evaluated on benchmarks covering agent capabilities, vision-language tasks, general visual question answering, STEM and reasoning, knowledge, and spatial intelligence.\n\n## Inference\n**Acceleration Engine:** vLLM\n**Precision:** FP8, NVFP4\n**Test Hardware:**\n* NVIDIA DGX Spark\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. Developers should work with their internal developer team to ensure these software components meet requirements for the relevant industry and use case and address 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",["D",1785450301123],"publisher","Qwen","latestVersion","versions","collectionCounts","imageUrl","/api/image/nim/qwen/models/qwen3.6-35b-a3b","rawImageUrl","canGuestDownload","latestVersionSizeInBytes",23690102536,"licenseTerms",{"_315":316,"_317":318,"_319":320,"_321":31,"_322":323},"isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[309],"displayLabels",[298,299],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_265":307,"_301":307,"_302":308,"_304":305},{"_265":300,"_301":300,"_302":303,"_304":305},"NSPECT-4L88-HB6N","value","filter",{"_306":305,"_301":300},"category","label","field","False",{"_306":305,"_301":307},{"_265":310,"_301":311,"_302":312,"_304":313},"NVIDIA NIM","nimmcro_nvidia_nim",{"_306":314,"_301":311},"feature","nvidia_nim","governingTerms","

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

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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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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,[548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569],[433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449],{"_450":543,"_265":543,"_209":210,"_423":544},{"_450":539,"_265":539,"_209":210,"_423":540},{"_450":534,"_265":534,"_209":210,"_423":535},{"_450":530,"_265":530,"_209":210,"_423":531},{"_450":525,"_265":525,"_209":210,"_423":526},{"_450":520,"_265":520,"_209":210,"_423":521},{"_450":512,"_265":512,"_209":210,"_423":513},{"_450":504,"_265":504,"_209":210,"_423":505},{"_450":499,"_265":499,"_209":210,"_423":500},{"_450":494,"_265":494,"_209":210,"_423":495},{"_450":489,"_265":489,"_209":210,"_423":490},{"_450":484,"_265":484,"_209":210,"_423":485},{"_450":479,"_265":479,"_209":210,"_423":480},{"_450":472,"_265":472,"_209":210,"_423":473},{"_450":467,"_265":467,"_209":210,"_423":468},{"_450":460,"_265":460,"_209":210,"_423":461},{"_450":451,"_265":451,"_209":210,"_423":452},"hostName","amazon-adsystem.com",[453,454],{"_83":459,"_456":451,"_457":31,"_209":458},{"_83":455,"_456":451,"_457":31,"_209":458},"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",[462,463],{"_83":466,"_456":460,"_457":31,"_209":465},{"_83":464,"_456":460,"_457":31,"_209":465},"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. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.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.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","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. 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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.",[640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664],[611,612,613,614,615],{"_450":525,"_265":525,"_209":210,"_423":635},{"_450":499,"_265":499,"_209":210,"_423":630},{"_450":625,"_265":625,"_209":210,"_423":626},{"_450":620,"_265":620,"_209":210,"_423":621},{"_450":616,"_265":616,"_209":210,"_423":617},"nvidia.partners",[618],{"_83":619,"_456":616,"_457":31,"_209":493},"edge","app.hushly.com",[622],{"_83":623,"_456":620,"_457":31,"_209":624},"*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",[627],{"_83":628,"_456":625,"_457":31,"_209":629},"_octo","This domain is owned by Github. 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We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","nv_country_code","developer.nvidia.com","This cookie is likely used to store the country code of the visitor to provide localized content and possibly for legal compliance with regional data privacy laws.","_gdxxxxxxxxxxxxx","nvidianews.nvidia.com","docs.api.nvidia.com","research.nvidia.com","readme_library","wcmmode","ai-podcast.nvidia.com","resources.nvidia.com","geo_locator","openedx-language-preference","learn.nvidia.com","doxygen_width","raytracing-docs.nvidia.com","This cookie is likely used to remember the width preferences of a sidebar or interface element within a documentation platform like Doxygen.","_sfid_eb78","This cookie is categorized as Functionality Cookie.","crowdsource.nvidia.com","mbox","Cookie set by Adobe SiteCatalyst analytics software - used for measuring the performance of page content using A/B split testing","readme_language","vid","This cookie name 'vid' is used for tracking the search request made by the user on the 'Find a practitioner' page.","at_check","Used by Adobe Target to check if cookies are enabled/supported on the browser","blogs.nvidia.com","nvHeadless","This cookie is likely used to track if a user is using the site in a 'headless' non-GUI mode, which could be for accessibility reasons or for automated processes.","C0002","Performance Cookies","These cookies are used to provide quantitative measures of our website visitors, such as the number of times you visit, time on page, your mouse movements, scrolling, clicks and keystroke activity on the websites; other browsing, search, or product research behavior; and what brought you to our site. These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751],[703,704,705,706],{"_450":718,"_265":718,"_209":210,"_423":719},{"_450":713,"_265":713,"_209":210,"_423":714},{"_450":512,"_265":512,"_209":210,"_423":710},{"_450":625,"_265":625,"_209":210,"_423":707},[708],{"_83":709,"_456":625,"_457":128,"_209":629},"_gh_sess",[711],{"_83":712,"_456":512,"_457":128,"_209":519},"YSC","bizible.com",[715],{"_83":716,"_456":713,"_457":31,"_209":717},"_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",[720],{"_83":721,"_456":718,"_457":31,"_209":722},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_83":793,"_456":571,"_457":31,"_209":794},{"_83":778,"_456":792,"_457":128,"_209":779},{"_83":761,"_456":574,"_457":31,"_209":686},{"_83":761,"_456":674,"_457":31,"_209":686},{"_83":790,"_456":571,"_457":31,"_209":791},{"_83":778,"_456":577,"_457":128,"_209":779},{"_83":788,"_456":571,"_457":31,"_209":789},{"_83":778,"_456":574,"_457":128,"_209":779},{"_83":786,"_456":673,"_457":31,"_209":787},{"_83":778,"_456":695,"_457":128,"_209":779},{"_83":758,"_456":574,"_457":31,"_209":760},{"_83":784,"_456":571,"_457":128,"_209":785},{"_83":782,"_456":571,"_457":31,"_209":783},{"_83":780,"_456":571,"_457":128,"_209":781},{"_83":778,"_456":669,"_457":128,"_209":779},{"_83":776,"_456":571,"_457":31,"_209":777},{"_83":774,"_456":571,"_457":31,"_209":775},{"_83":772,"_456":571,"_457":128,"_209":773},{"_83":770,"_456":678,"_457":31,"_209":771},{"_83":758,"_456":669,"_457":31,"_209":760},{"_83":768,"_456":571,"_457":31,"_209":769},{"_83":766,"_456":571,"_457":128,"_209":767},{"_83":764,"_456":571,"_457":31,"_209":765},{"_83":762,"_456":571,"_457":31,"_209":763},{"_83":761,"_456":571,"_457":31,"_209":686},{"_83":758,"_456":759,"_457":31,"_209":760},{"_83":756,"_456":571,"_457":31,"_209":757},{"_83":754,"_456":571,"_457":128,"_209":755},{"_83":752,"_456":571,"_457":31,"_209":753},"_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

checksums.blake3	499 B	July 30, 2026 UTC	curl wget Download
config.json	1.19 KB	July 30, 2026 UTC	curl wget Download
configuration.json	73 B	July 30, 2026 UTC	curl wget Download
model.safetensors	736.06 MB	July 30, 2026 UTC	curl wget Download
README.md	6.59 KB	July 30, 2026 UTC	curl wget Download