SGLang

SGLang is a fast serving framework for large language models and vision language models. It makes your interaction with models faster and more controllable by co-designing the backend runtime and frontend language.

NGC Containers are one of the easiest ways to get started with SGLang. The SGLang NGC Container comes with all dependencies included, providing an easy place to start developing and deploying common applications, such as conversational AI, natural language processing (NLP), recommenders, and computer vision.

The SGLang NGC Container is optimized for GPU acceleration, and contains a validated set of libraries that enable and optimize GPU performance.

Prerequisites

Using the SGLang NGC Container requires the host system to have the following installed:

For supported versions, see the Framework Containers Support Matrix and the NVIDIA Container Toolkit Documentation.

No other installation, compilation, or dependency management is required. It is not necessary to install the NVIDIA CUDA Toolkit.

Running SGLang Using Docker

To run a container, issue the appropriate command as explained in the Running A Container chapter in the NVIDIA Containers For Deep Learning Frameworks User’s Guide and specify the registry, repository, and tags. For more information about using NGC, refer to the NGC Container User Guide.

Some Hugging Face models need tokens to be able to download them.

If you have Docker 19.03 or later, a typical command to launch the container is:

docker run --gpus all -it --rm nvcr.io/nvidia/sglang:YY.MM-py3\n

If you have Docker 19.02 or earlier, a typical command to launch the container is:

nvidia-docker run -it --rm -v nvcr.io/nvidia/sglang:YY.MM-py3\n

Where:

YY.MM is the container version. For example, 25.10.

SGLang can be deployed in a client–server configuration. Start the HTTP inference server inside the container:

python3 -m sglang.launch_server --model-path nvidia/Llama-3.1-8B-Instruct-FP4 --host 0.0.0.0 --port 30000 --trust-remote-code --tp 1 --quantization modelopt_fp4 &\n

From a client, issue a text-generation request by POST-ing to /generate with a JSON body containing the prompt and sampling parameters:

curl http://localhost:30000/v1/chat/completions \\\n      -H \"Content-Type: application/json\" \\\n      -d '{ \"model\": \"nvidia/Llama-3.1-8B-Instruct-FP4\", \"messages\": [{\"role\": \"user\", \"content\": \"What is NVIDIA famous for?\"}], \"max_tokens\": 1000 }'\n

You might want to pull in data and model descriptions from locations outside the container for use by SGLang. To accomplish this, the easiest method is to mount one or more host directories as Docker bind mounts. For example:

docker run --gpus all -it --rm -v local_dir:container_dir nvcr.io/nvidia/sglang:YY.MM-py3\n

Notes:

Using some Hugging Face models requires HF authentication:

hf auth login  --token [YOUR_TOKEN]\n

Please refer to [this guide] (https://huggingface.co/docs/hub/en/security-tokens#user-access-tokens) for how to generate HF tokens.

What Is In This Container?

For the full list of contents, see the SGLang Container Release Notes.

This container image contains the complete source of the version of SGLang in /opt/sglang. It is pre-built and installed in Conda default environment (/opt/conda/lib/python3.8/site-packages/torch/) in the container image. Visit SGLang Docs to learn more about SGLang.

The NVIDIA SGLang Container is optimized for use with NVIDIA GPUs, and contains the following software for GPU acceleration:

The software stack in this container has been validated for compatibility, and does not require any additional installation or compilation from the end user. This container can help accelerate your deep learning workflow from end to end.

Link to Open Source Code

Security Common Vulnerabilities and Exposures (CVEs)

Please review the Security Scanning tab on NGC to view the latest security scan results.\nFor certain open-source vulnerabilities listed in the scan results, NVIDIA provides a response in the form of a Vulnerability Exploitability eXchange (VEX) document. The VEX information can be reviewed and downloaded from the Security Scanning tab.

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 developer team to ensure this container meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

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

License

By pulling and using the container, you accept the terms and conditions of this End User License Agreement and Product-Specific Terms.

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The NVIDIA SGLang NGC Container is optimized for GPU acceleration, and contains a validated set of libraries that enable and optimize GPU performance.","## SGLang\n\n[SGLang](https://docs.sglang.ai/) is a fast serving framework for large language models and vision language models. It makes your interaction with models faster and more controllable by co-designing the backend runtime and frontend language.\n\nNGC Containers are one of the easiest ways to get started with SGLang. The SGLang NGC Container comes with all dependencies included, providing an easy place to start developing and deploying common applications, such as conversational AI, natural language processing (NLP), recommenders, and computer vision.\n\nThe SGLang NGC Container is optimized for GPU acceleration, and contains a validated set of libraries that enable and optimize GPU performance. \n### Prerequisites\n\nUsing the SGLang NGC Container requires the host system to have the following installed:\n\n* [Docker Engine](https://docs.docker.com/get-docker/)\n* [NVIDIA GPU Drivers](https://docs.nvidia.com/datacenter/tesla/tesla-installation-notes/index.html)\n* [NVIDIA Container Toolkit](https://github.com/NVIDIA/nvidia-docker)\n\nFor supported versions, see the [Framework Containers Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html) and the [NVIDIA Container Toolkit Documentation](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html).\n\nNo other installation, compilation, or dependency management is required. *It is not necessary to install the NVIDIA CUDA Toolkit.*\n\n### Running SGLang Using Docker\n\nTo run a container, issue the appropriate command as explained in the [Running A Container](https://docs.nvidia.com/deeplearning/frameworks/user-guide/index.html#runcont) chapter in the *NVIDIA Containers For Deep Learning Frameworks User’s Guide* and specify the registry, repository, and tags. For more information about using NGC, refer to the [NGC Container User Guide](https://docs.nvidia.com/ngc/ngc-catalog-user-guide/index.html).\n\nSome Hugging Face models need tokens to be able to download them. \n\nIf you have Docker 19.03 or later, a typical command to launch the container is:\n\n```\ndocker run --gpus all -it --rm nvcr.io/nvidia/sglang:YY.MM-py3\n```\n\nIf you have Docker 19.02 or earlier, a typical command to launch the container is:\n\n```\nnvidia-docker run -it --rm -v nvcr.io/nvidia/sglang:YY.MM-py3\n```\n\nWhere:\n\n- `YY.MM` is the container version. For example, `25.10`.\n\nSGLang can be deployed in a client–server configuration. Start the HTTP inference server inside the container:\n```\npython3 -m sglang.launch_server --model-path nvidia/Llama-3.1-8B-Instruct-FP4 --host 0.0.0.0 --port 30000 --trust-remote-code --tp 1 --quantization modelopt_fp4 &\n```\n\nFrom a client, issue a text-generation request by POST-ing to /generate with a JSON body containing the prompt and sampling parameters:\n```\ncurl http://localhost:30000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{ \"model\": \"nvidia/Llama-3.1-8B-Instruct-FP4\", \"messages\": [{\"role\": \"user\", \"content\": \"What is NVIDIA famous for?\"}], \"max_tokens\": 1000 }'\n```\n\nYou might want to pull in data and model descriptions from locations outside the container for use by SGLang. To accomplish this, the easiest method is to mount one or more host directories as [Docker bind mounts](https://docs.docker.com/storage/bind-mounts/). For example:\n\n```\ndocker run --gpus all -it --rm -v local_dir:container_dir nvcr.io/nvidia/sglang:YY.MM-py3\n```\n\nNotes:\n\n1. Using some Hugging Face models requires HF authentication:\n```\nhf auth login --token [YOUR_TOKEN]\n```\nPlease refer to [this guide] (https://huggingface.co/docs/hub/en/security-tokens#user-access-tokens) for how to generate HF tokens.\n\n### What Is In This Container?\n\nFor the full list of contents, see the [SGLang Container Release Notes](https://docs.nvidia.com/deeplearning/frameworks/sglang-release-notes/index.html).\n\nThis container image contains the complete source of the version of SGLang in /opt/sglang. It is pre-built and installed in Conda default environment (/opt/conda/lib/python3.8/site-packages/torch/) in the container image. Visit [SGLang Docs](https://docs.sglang.ai/) to learn more about SGLang.\n\nThe NVIDIA SGLang Container is optimized for use with NVIDIA GPUs, and contains the following software for GPU acceleration:\n\n- [CUDA](https://developer.nvidia.com/cuda-toolkit)\n- [cuBLAS](https://developer.nvidia.com/cublas)\n- [NVIDIA cuDNN](https://developer.nvidia.com/cudnn)\n- [NVIDIA NCCL](https://developer.nvidia.com/nccl) (optimized for [NVLink](https://www.nvidia.com/object/nvlink.html))\n- [NVIDIA Data Loading Library (DALI)](https://developer.nvidia.com/dali)\n- [PyTorch](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch)\n\nThe software stack in this container has been validated for compatibility, and does not require any additional installation or compilation from the end user. This container can help accelerate your deep learning workflow from end to end.\n\n[Link to Open Source Code](https://developer.download.nvidia.com/assets/dlfw/)\n\n### Suggested Reading\n\nFor the latest Release Notes, see the [SGLang Release Notes](https://docs.nvidia.com/deeplearning/frameworks/sglang-release-notes/index.html).\n\nFor a full list of the supported software and specific versions that come packaged with this framework based on the container image, see the [Frameworks Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html).\n\nFor more information about SGLang, including tutorials, documentation, and examples, see:\n\n* [SGLang Docs](https://docs.sglang.ai/)\n* [SGLang project](https://github.com/sgl-project/sglang)\n\n### Security Common Vulnerabilities and Exposures (CVEs)\nPlease review the Security Scanning tab on NGC to view the latest security scan results.\nFor certain open-source vulnerabilities listed in the scan results, NVIDIA provides a response in the form of a Vulnerability Exploitability eXchange (VEX) document. The VEX information can be reviewed and downloaded from the Security Scanning tab.\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 developer team to ensure this container meets requirements for the relevant industry and use case and addresses unforeseen product misuse. \n\nPlease report quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n### License\n\nBy pulling and using the container, you accept the terms and conditions of this [End User License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and [Product-Specific Terms](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/).","SGLang",["D",1790625592109],[270,268,266,264,262,260,258,256,254,252,250,248,181],"/api/image/nvidia/-/containers/sglang",10357326033,"hasSignedTag",[],[298,299,300,301,302,303,304,305,306,307,308,309],[],[290],[],{"_90":291,"_292":291,"_293":294,"_295":296},"signed images","value","filter",{"_297":296,"_292":291},"category","system","field",{"_90":345,"_292":346,"_293":347,"_295":313},{"_90":342,"_292":343,"_293":344,"_295":313},{"_90":339,"_292":340,"_293":341,"_295":313},{"_90":337,"_292":337,"_293":338,"_295":313},{"_90":334,"_292":335,"_293":336,"_295":313},{"_90":331,"_292":332,"_293":333,"_295":313},{"_90":328,"_292":329,"_293":330,"_295":313},{"_90":325,"_292":326,"_293":327,"_295":313},{"_90":322,"_292":323,"_293":324,"_295":313},{"_90":318,"_292":319,"_293":320,"_295":313},{"_90":315,"_292":316,"_293":317,"_295":313},{"_90":310,"_292":311,"_293":312,"_295":313},"Translation","uscs_translation",{"_297":314,"_292":311},"label","application","Question Answering","uscs_question_answering",{"_297":314,"_292":316},"NVIDIA AI","soln_nvidia_ai",{"_297":321,"_292":319},"technology","Natural Language Understanding","uscs_natural_language_understanding",{"_297":314,"_292":323},"Natural Language Processing","uscs_natural_language_processing",{"_297":314,"_292":326},"ML","soln_ml",{"_297":321,"_292":329},"Inference","soln_inference",{"_297":321,"_292":332},"High Performance Computing","uscs_high_performance_computing",{"_297":314,"_292":335},"DLFW",{"_297":313,"_292":337},"DL","soln_dl",{"_297":321,"_292":340},"Conversational AI","soln_conversational_ai",{"_297":321,"_292":343},"AI","soln_ai",{"_297":321,"_292":346},{"_148":1478,"_150":1479},"ngcApps",[1431],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1412":1413,"_1414":1415,"_1416":1417,"_1418":1419,"_1420":359,"_180":385,"_1421":1422,"_1423":51,"_1424":126,"_1425":126,"_1426":1427,"_1428":126,"_1429":1430},"amplitude",{"_1409":1410,"_1021":-7,"_1411":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1403":1404,"_1405":1406,"_1407":1408},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1791157709301,"1.19.0","defaultSearchSuggestions",{"_1291":103,"_1292":1293,"_1294":1295,"_1296":1297},"announcements",[],"consentState",{"_424":1281,"_1282":26,"_1283":26,"_1284":126,"_1285":1286},"consentConfig",{"_406":407,"_408":409,"_410":411,"_412":413,"_414":415,"_416":417,"_418":419,"_420":421,"_422":423,"_424":425},"showContactAdmin",[],[],[398,399,400,401,402,403,404],{"_40":62,"_42":66,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":62,"_42":64,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":62,"_42":63,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":56,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":61,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":405,"_50":51},6,"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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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.","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.",[696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726],[662,663,664,665,666,667],{"_466":548,"_90":548,"_89":103,"_438":691},{"_466":523,"_90":523,"_89":103,"_438":686},{"_466":681,"_90":681,"_89":103,"_438":682},{"_466":676,"_90":676,"_89":103,"_438":677},{"_466":672,"_90":672,"_89":103,"_438":673},{"_466":668,"_90":668,"_89":103,"_438":669},"nvcrm.my.site.com",[670],{"_246":671,"_472":668,"_473":26,"_89":103},"BrowserId","nvidia.partners",[674],{"_246":675,"_472":672,"_473":26,"_89":509},"edge","app.hushly.com",[678],{"_246":679,"_472":676,"_473":26,"_89":680},"*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",[683],{"_246":684,"_472":681,"_473":26,"_89":685},"_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.",[796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839],[768,769,770,771,772],{"_466":791,"_90":791,"_89":103,"_438":792},{"_466":786,"_90":786,"_89":103,"_438":787},{"_466":536,"_90":536,"_89":103,"_438":783},{"_466":776,"_90":776,"_89":103,"_438":777},{"_466":681,"_90":681,"_89":103,"_438":773},[774],{"_246":775,"_472":681,"_473":126,"_89":685},"_gh_sess","statcounter.com",[778,779],{"_246":782,"_472":776,"_473":26,"_89":781},{"_246":780,"_472":776,"_473":26,"_89":781},"is_unique","This domain is owned by StatCounter.\nThe main business activity is: Real time visitor analytics","is_visitor_unique",[784],{"_246":785,"_472":536,"_473":126,"_89":542},"YSC","bizible.com",[788],{"_246":789,"_472":786,"_473":26,"_89":790},"_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",[793],{"_246":794,"_472":791,"_473":26,"_89":795},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_246":901,"_472":610,"_473":26,"_89":902},{"_246":899,"_472":610,"_473":126,"_89":900},{"_246":896,"_472":897,"_473":26,"_89":898},{"_246":874,"_472":887,"_473":126,"_89":875},{"_246":894,"_472":610,"_473":26,"_89":895},{"_246":848,"_472":613,"_473":26,"_89":744},{"_246":848,"_472":646,"_473":26,"_89":744},{"_246":892,"_472":610,"_473":26,"_89":893},{"_246":876,"_472":731,"_473":126,"_89":877},{"_246":874,"_472":616,"_473":126,"_89":875},{"_246":890,"_472":610,"_473":26,"_89":891},{"_246":888,"_472":610,"_473":26,"_89":889},{"_246":882,"_472":610,"_473":26,"_89":103},{"_246":873,"_472":887,"_473":26,"_89":517},{"_246":874,"_472":613,"_473":126,"_89":875},{"_246":885,"_472":646,"_473":26,"_89":886},{"_246":883,"_472":736,"_473":26,"_89":884},{"_246":874,"_472":760,"_473":126,"_89":875},{"_246":882,"_472":731,"_473":26,"_89":103},{"_246":846,"_472":613,"_473":26,"_89":847},{"_246":873,"_472":613,"_473":26,"_89":517},{"_246":880,"_472":610,"_473":126,"_89":881},{"_246":878,"_472":610,"_473":26,"_89":879},{"_246":876,"_472":610,"_473":126,"_89":877},{"_246":874,"_472":731,"_473":126,"_89":875},{"_246":873,"_472":731,"_473":26,"_89":517},{"_246":871,"_472":610,"_473":26,"_89":872},{"_246":869,"_472":610,"_473":26,"_89":870},{"_246":867,"_472":610,"_473":126,"_89":868},{"_246":864,"_472":865,"_473":26,"_89":866},{"_246":846,"_472":731,"_473":26,"_89":847},{"_246":862,"_472":610,"_473":126,"_89":863},{"_246":860,"_472":610,"_473":26,"_89":861},{"_246":859,"_472":731,"_473":126,"_89":854},{"_246":857,"_472":610,"_473":126,"_89":858},{"_246":855,"_472":625,"_473":26,"_89":856},{"_246":853,"_472":731,"_473":126,"_89":854},{"_246":851,"_472":610,"_473":26,"_89":852},{"_246":849,"_472":610,"_473":26,"_89":850},{"_246":848,"_472":610,"_473":26,"_89":744},{"_246":846,"_472":625,"_473":26,"_89":847},{"_246":844,"_472":610,"_473":26,"_89":845},{"_246":842,"_472":610,"_473":126,"_89":843},{"_246":840,"_472":610,"_473":26,"_89":841},"_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","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.","s_tp","This cookie is associated with Adobe Analytics. The cookie is used for measuring a visitor’s scroll activity to see how much of a page they view before moving on to another page.","_an_uid","This cookie is likely used for analytics purposes to track user behavior and site usage, which may include unique user identification for tracking.","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.","s_ips","_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.","s_plt","This cookie is used to measure the time taken for pages to load, allowing website owners to identify slow loading pages and improve user experience.","_session_id","resources.nvidia.com","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. It is used to persist the Hotjar User ID, unique to that site on the browser. 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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.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. 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This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. 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.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. 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.","nvidiaCart","This domain is owned by NVIDIA Corporation which provides information about the company, its products, solutions, industries, news, events, and career opportunities.","nv_co","__uxq412__ses.88c1","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. 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.","learn.learn.nvidia.com","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","This cookie is associated with Akamai. 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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","buildapi.ngc.nvidia.com","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","_pf_consent","This cookie name is associated with Command Alkon Incorporated which is used to store user consent status and expiration.","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. It is a temporary cookie used to determine if the browser accepts cookies.","XSRF-TOKEN","This cookie is written to help with site security in preventing Cross-Site Request Forgery attacks.This cookie helps with site security in preventing Cross-Site Request Forgery attacks.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","bm_ss","This cookie is used to help protect websites from automated bot traffic.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. 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