vLLM

vLLM is a fast and easy-to-use library for LLM inference and serving.\nOriginally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry.

NGC Containers are one of the easiest ways to get started with vLLM. The vLLM 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 vLLM NGC Container is optimized for GPU acceleration, and contains a validated set of libraries that enable and optimize GPU performance.

Prerequisites

Using the vLLM 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 vLLM 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.

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/vllm:xx.xx-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/vllm:xx.xx-py3\n

Where:

xx.xx is the container version. For example, 25.09.

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

python3 -m vllm.entrypoints.openai.api_server --model nvidia/Llama-3.1-8B-Instruct-FP8 --trust-remote-code --tensor-parallel-size 1 --max-model-len 1024 --gpu-memory-utilization 0.85\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 -X 'POST' \\\n'http://0.0.0.0:8000/v1/chat/completions' \\\n-H 'accept: application/json' \\\n-H 'Content-Type: application/json' \\\n-d '{\n\"model\": \"nvidia/Llama-3.1-8B-Instruct-FP8\",\n\"messages\": [{\"role\":\"user\", \"content\": \"What is NVIDIA famous for?\"}]\n}'\n

Notes:

Using some HuggingFace 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.

On Jetson Thor and NVIDIA DGX Spark, if you run into out-of-memory (OOM) issues when starting the server, you might want to clean the memory cache before running the vLLM server:

/proc/sys/vm/drop_caches\n\">

sync && echo 3 > /proc/sys/vm/drop_caches\n

See /workspace/README.md inside the container for information on getting started and customizing your vLLM image.

You might want to pull in data and model descriptions from locations outside the container for use by vLLM. 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/vllm:xx.xx-py3\n

What Is In This Container?

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

This container image contains the complete source of the version of vLLM in /opt/vllm. It is pre-built and installed in the default system Python environment (/usr/local/lib/python3.12/dist-packages/vllm) in the container image. Visit vLLM Docs to learn more about vLLM.

The NVIDIA vLLM 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 model 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 vLLM NGC Container is optimized for GPU acceleration, and contains a validated set of libraries that enable and optimize GPU performance.","## vLLM\n\n[vLLM](https://docs.vllm.ai/en/latest/) is a fast and easy-to-use library for LLM inference and serving.\nOriginally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry.\n\nNGC Containers are one of the easiest ways to get started with vLLM. The vLLM 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 vLLM NGC Container is optimized for GPU acceleration, and contains a validated set of libraries that enable and optimize GPU performance.\n\n### Prerequisites\n\nUsing the vLLM 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 vLLM 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\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/vllm:xx.xx-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/vllm:xx.xx-py3\n```\n\nWhere:\n\n- `xx.xx` is the container version. For example, `25.09`.\n\nvLLM can be deployed in a client–server configuration. Start the HTTP inference server inside the container:\n```\npython3 -m vllm.entrypoints.openai.api_server --model nvidia/Llama-3.1-8B-Instruct-FP8 --trust-remote-code --tensor-parallel-size 1 --max-model-len 1024 --gpu-memory-utilization 0.85\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 -X 'POST' \\\n'http://0.0.0.0:8000/v1/chat/completions' \\\n-H 'accept: application/json' \\\n-H 'Content-Type: application/json' \\\n-d '{\n\"model\": \"nvidia/Llama-3.1-8B-Instruct-FP8\",\n\"messages\": [{\"role\":\"user\", \"content\": \"What is NVIDIA famous for?\"}]\n}'\n```\n\nNotes:\n\n1. Using some HuggingFace models requires HF authentication:\n```\nhf auth login --token [YOUR_TOKEN]\n```\n. Please refer to [this guide] (https://huggingface.co/docs/hub/en/security-tokens#user-access-tokens) for how to generate HF tokens.\n \n2. On Jetson Thor and NVIDIA DGX Spark, if you run into out-of-memory (OOM) issues when starting the server, you might want to clean the memory cache before running the vLLM server:\n```\nsync && echo 3 > /proc/sys/vm/drop_caches\n``` \n\nSee **`/workspace/README.md`** inside the container for information on getting started and customizing your vLLM image.\n\nYou might want to pull in data and model descriptions from locations outside the container for use by vLLM. 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/vllm:xx.xx-py3\n```\n### What Is In This Container?\n\nFor the full list of contents, see the [vLLM Container Release Notes](https://docs.nvidia.com/deeplearning/frameworks/vllm-release-notes/index.html).\n\nThis container image contains the complete source of the version of vLLM in /opt/vllm. It is pre-built and installed in the default system Python environment (/usr/local/lib/python3.12/dist-packages/vllm) in the container image. Visit [vLLM Docs](https://docs.vllm.ai/en/latest/) to learn more about vLLM.\n\nThe NVIDIA vLLM 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 [vLLM Release Notes](https://docs.nvidia.com/deeplearning/frameworks/vllm-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 vLLM, including tutorials, documentation, and examples, see:\n\n* [vLLM Docs](https://docs.vllm.ai/en/latest/)\n* [vLLM project](https://github.com/vllm-project/vllm)\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 model 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/).","vLLM",["D",1780435634226],[257,255,253,176,250,248,246,244,242,240,238,235],"/api/image/nvidia/-/containers/vllm",9603085145,"hasSignedTag",[],[285,286,287,288,289,290,291,292,293,294,295,296,297,298],[],[277],[],{"_91":278,"_279":278,"_280":281,"_282":283},"signed images","value","filter",{"_284":283,"_279":278},"category","system","field",{"_91":340,"_279":341,"_280":342,"_282":302},{"_91":337,"_279":338,"_280":339,"_282":302},{"_91":334,"_279":335,"_280":336,"_282":302},{"_91":332,"_279":332,"_280":333,"_282":302},{"_91":329,"_279":330,"_280":331,"_282":302},{"_91":325,"_279":326,"_280":327,"_282":302},{"_91":322,"_279":323,"_280":324,"_282":302},{"_91":319,"_279":320,"_280":321,"_282":302},{"_91":316,"_279":317,"_280":318,"_282":302},{"_91":313,"_279":314,"_280":315,"_282":302},{"_91":311,"_279":311,"_280":312,"_282":302},{"_91":307,"_279":308,"_280":309,"_282":302},{"_91":304,"_279":305,"_280":306,"_282":302},{"_91":299,"_279":300,"_280":301,"_282":302},"Translation","uscs_translation",{"_284":303,"_279":300},"label","application","Question Answering","uscs_question_answering",{"_284":303,"_279":305},"NVIDIA AI","soln_nvidia_ai",{"_284":310,"_279":308},"technology","NSPECT-EQZO-3F6K",{"_284":302,"_279":311},"Natural Language Understanding","uscs_natural_language_understanding",{"_284":303,"_279":314},"Natural Language Processing","uscs_natural_language_processing",{"_284":303,"_279":317},"ML","soln_ml",{"_284":310,"_279":320},"Inference","soln_inference",{"_284":310,"_279":323},"HPC / Supercomputing","indus_hpc_supercomputing",{"_284":328,"_279":326},"industry","High Performance Computing","uscs_high_performance_computing",{"_284":303,"_279":330},"DLFW",{"_284":302,"_279":332},"DL","soln_dl",{"_284":310,"_279":335},"Conversational AI","soln_conversational_ai",{"_284":310,"_279":338},"AI","soln_ai",{"_284":310,"_279":341},{"_147":1523,"_149":1524},"ngcApps",[1476],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1458":1459,"_1460":1461,"_1462":1463,"_1464":1465,"_1466":354,"_175":378,"_1467":1468,"_1456":51,"_1469":126,"_1470":126,"_1471":1472,"_1473":126,"_1474":1475},"amplitude",{"_1454":1455,"_1456":51,"_1051":-7,"_1457":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1448":1449,"_1450":1451,"_1452":1453},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.4.0","defaultSearchSuggestions",{"_1326":104,"_1327":1328,"_1329":1330,"_1331":1332},"announcements",[],"consentState",{"_416":1316,"_1317":26,"_1318":26,"_1319":126,"_1320":1321},"consentConfig",{"_398":399,"_400":401,"_402":403,"_404":405,"_406":407,"_408":409,"_410":411,"_412":413,"_414":415,"_416":417},"showContactAdmin",1782572361157,[],[],[392,393,394,395,396,397],{"_40":62,"_42":67,"_44":142,"_46":-5,"_48":-5,"_50":51},{"_40":62,"_42":65,"_44":142,"_46":-5,"_48":-5,"_50":51},{"_40":62,"_42":63,"_44":142,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":142,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":45,"_46":47,"_48":49,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":49,"_50":51},"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. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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It is used as a URL shortening service, which allows Twitter to collect data on how links are shared.","snowplow.com",[496],{"_236":497,"_463":494,"_464":26,"_90":498},"sp","Testing","google.com",[501],{"_236":502,"_463":499,"_464":26,"_90":503},"NID","This domain is owned by Google Inc. Although Google is primarily known as a search engine, the company provides a diverse range of products and services. Its main source of revenue however is advertising. Google tracks users extensively both through its own products and sites, and the numerous technologies embedded into many millions of websites around the world. It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","tags.srv.stackadapt.com",[506,507,508,509],{"_236":514,"_463":504,"_464":26,"_90":511},{"_236":513,"_463":504,"_464":26,"_90":511},{"_236":512,"_463":504,"_464":26,"_90":511},{"_236":510,"_463":504,"_464":26,"_90":511},"sa-user-id-v3","This domain is associated with StackAdapt, a platform for programmatic advertising, which suggests that it is used for targeting and advertising purposes.","sa-user-id","sa-user-id-v2","sa-user-id-v4","linkedin.com",[517],{"_236":518,"_463":515,"_464":26,"_90":519},"bcookie","This domain is owned by LinkedIn, the business networking platform. It typically acts as a third party host where website owners have placed one of its content sharing buttons in their pages, although its content and services can be embedded in other ways. Although such buttons add functionality to the website they are on, cookies are set regardless of whether or not the visitor has an active Linkedin profile, or agreed to their terms and conditions. For this reason it is classified as a primarily tracking/targeting domain.","twitter.com",[522,523,524,525],{"_236":531,"_463":520,"_464":26,"_90":527},{"_236":529,"_463":520,"_464":26,"_90":530},{"_236":528,"_463":520,"_464":26,"_90":527},{"_236":526,"_463":520,"_464":26,"_90":527},"personalization_id","This domain is owned by Twitter. The main business activity is: Social Networking Services. Where twitter acts as a third party host, it collects data through a range of plug-ins and integrations, that is primarily used for tracking and targeting.","guest_id_marketing","guest_id","Twitter does not currently provide information on the use of specific cookies.","guest_id_ads","youtube.com",[534,535,536,537],{"_236":543,"_463":532,"_464":26,"_90":541},{"_236":542,"_463":532,"_464":26,"_90":541},{"_236":540,"_463":532,"_464":26,"_90":541},{"_236":538,"_463":532,"_464":26,"_90":539},"VISITOR_INFO1_LIVE","This cookie is used as a unique identifier to track viewing of videos","__Secure-YNID","YouTube is a Google owned platform for hosting and sharing videos. YouTube collects user data through videos embedded in websites, which is aggregated with profile data from other Google services in order to display targeted advertising to web visitors across a broad range of their own and other websites.","__Secure-YEC","__Secure-ROLLOUT_TOKEN","demdex.net",[546],{"_236":547,"_463":544,"_464":26,"_90":548},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[551],{"_236":552,"_463":549,"_464":26,"_90":553},"receive-cookie-deprecation","This domain is owned by im-apps.net which provides information and resources about various instant messaging (IM) applications to help users make informed decisions based on their communication needs.","influ2.com",[556],{"_236":557,"_463":554,"_464":26,"_90":498},"R","6sc.co",[560],{"_236":561,"_463":558,"_464":26,"_90":562},"6suuid","This domain is associated with the company 6Sense Insights, Inc., which provides predictive analytics and account engagement services. 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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. This is a pattern type cookie where a random 13-digit number is appended to the prefix _cs_.","_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","_twpid","This cookie is likely used to track user identity by assigning a unique user ID when the user engages with the website. 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,[690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710],[655,656,657,658,659],{"_457":549,"_91":549,"_90":104,"_430":681},{"_457":515,"_91":515,"_90":104,"_430":676},{"_457":669,"_91":669,"_90":104,"_430":670},{"_457":665,"_91":665,"_90":104,"_430":666},{"_457":660,"_91":660,"_90":104,"_430":661},"nvcrm.my.site.com",[662],{"_236":663,"_463":660,"_464":26,"_90":664},"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",[667],{"_236":668,"_463":665,"_464":26,"_90":498},"edge","github.com",[671,672],{"_236":675,"_463":669,"_464":26,"_90":674},{"_236":673,"_463":669,"_464":26,"_90":674},"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. The data is aggregated and analyzed; therefore, it remains anonymous upon use.\n","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.","_biz_nA","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 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