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",1787946497067],[274,272,270,268,266,264,262,179,259,257,255,253,251,249,246],"/api/image/nvidia/-/containers/vllm",11709742656,"hasSignedTag",[],[302,303,304,305,306,307,308,309,310,311,312,313,314,315],[],[294],[],{"_89":295,"_296":295,"_297":298,"_299":300},"signed images","value","filter",{"_301":300,"_296":295},"category","system","field",{"_89":357,"_296":358,"_297":359,"_299":319},{"_89":354,"_296":355,"_297":356,"_299":319},{"_89":351,"_296":352,"_297":353,"_299":319},{"_89":349,"_296":349,"_297":350,"_299":319},{"_89":346,"_296":347,"_297":348,"_299":319},{"_89":342,"_296":343,"_297":344,"_299":319},{"_89":339,"_296":340,"_297":341,"_299":319},{"_89":336,"_296":337,"_297":338,"_299":319},{"_89":333,"_296":334,"_297":335,"_299":319},{"_89":330,"_296":331,"_297":332,"_299":319},{"_89":328,"_296":328,"_297":329,"_299":319},{"_89":324,"_296":325,"_297":326,"_299":319},{"_89":321,"_296":322,"_297":323,"_299":319},{"_89":316,"_296":317,"_297":318,"_299":319},"Translation","uscs_translation",{"_301":320,"_296":317},"label","application","Question Answering","uscs_question_answering",{"_301":320,"_296":322},"NVIDIA AI","soln_nvidia_ai",{"_301":327,"_296":325},"technology","NSPECT-EQZO-3F6K",{"_301":319,"_296":328},"Natural Language Understanding","uscs_natural_language_understanding",{"_301":320,"_296":331},"Natural Language Processing","uscs_natural_language_processing",{"_301":320,"_296":334},"ML","soln_ml",{"_301":327,"_296":337},"Inference","soln_inference",{"_301":327,"_296":340},"HPC / Supercomputing","indus_hpc_supercomputing",{"_301":345,"_296":343},"industry","High Performance Computing","uscs_high_performance_computing",{"_301":320,"_296":347},"DLFW",{"_301":319,"_296":349},"DL","soln_dl",{"_301":327,"_296":352},"Conversational AI","soln_conversational_ai",{"_301":327,"_296":355},"AI","soln_ai",{"_301":327,"_296":358},{"_146":1334,"_148":1335},"ngcApps",[1287],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1268":1269,"_1270":1271,"_1272":1273,"_1274":1275,"_1276":371,"_178":397,"_1277":1278,"_1279":51,"_1280":124,"_1281":124,"_1282":1283,"_1284":124,"_1285":1286},"amplitude",{"_1265":1266,"_910":-7,"_1267":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1259":1260,"_1261":1262,"_1263":1264},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1788161858504,"1.12.0","defaultSearchSuggestions",{"_1150":102,"_1151":1152,"_1153":1154,"_1155":1156},"announcements",[],"consentState",{"_436":1140,"_1141":26,"_1142":26,"_1143":124,"_1144":1145},"consentConfig",{"_418":419,"_420":421,"_422":423,"_424":425,"_426":427,"_428":429,"_430":431,"_432":433,"_434":435,"_436":437},"showContactAdmin",1788161858505,[],[],[411,412,413,414,415,416,417],{"_40":62,"_42":65,"_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":61,"_46":-5,"_48":-5,"_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. 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We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",[574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595],[459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475],{"_476":569,"_89":569,"_88":102,"_450":570},{"_476":565,"_89":565,"_88":102,"_450":566},{"_476":560,"_89":560,"_88":102,"_450":561},{"_476":556,"_89":556,"_88":102,"_450":557},{"_476":551,"_89":551,"_88":102,"_450":552},{"_476":546,"_89":546,"_88":102,"_450":547},{"_476":538,"_89":538,"_88":102,"_450":539},{"_476":530,"_89":530,"_88":102,"_450":531},{"_476":525,"_89":525,"_88":102,"_450":526},{"_476":520,"_89":520,"_88":102,"_450":521},{"_476":515,"_89":515,"_88":102,"_450":516},{"_476":510,"_89":510,"_88":102,"_450":511},{"_476":505,"_89":505,"_88":102,"_450":506},{"_476":498,"_89":498,"_88":102,"_450":499},{"_476":493,"_89":493,"_88":102,"_450":494},{"_476":486,"_89":486,"_88":102,"_450":487},{"_476":477,"_89":477,"_88":102,"_450":478},"hostName","amazon-adsystem.com",[479,480],{"_247":485,"_482":477,"_483":26,"_88":484},{"_247":481,"_482":477,"_483":26,"_88":484},"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",[488,489],{"_247":492,"_482":486,"_483":26,"_88":491},{"_247":490,"_482":486,"_483":26,"_88":491},"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.",4,[667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691],[638,639,640,641,642],{"_476":551,"_89":551,"_88":102,"_450":662},{"_476":525,"_89":525,"_88":102,"_450":657},{"_476":652,"_89":652,"_88":102,"_450":653},{"_476":647,"_89":647,"_88":102,"_450":648},{"_476":643,"_89":643,"_88":102,"_450":644},"nvidia.partners",[645],{"_247":646,"_482":643,"_483":26,"_88":519},"edge","app.hushly.com",[649],{"_247":650,"_482":647,"_483":26,"_88":651},"*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",[654],{"_247":655,"_482":652,"_483":26,"_88":656},"_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.",[750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778],[730,731,732,733],{"_476":745,"_89":745,"_88":102,"_450":746},{"_476":740,"_89":740,"_88":102,"_450":741},{"_476":538,"_89":538,"_88":102,"_450":737},{"_476":652,"_89":652,"_88":102,"_450":734},[735],{"_247":736,"_482":652,"_483":124,"_88":656},"_gh_sess",[738],{"_247":739,"_482":538,"_483":124,"_88":545},"YSC","bizible.com",[742],{"_247":743,"_482":740,"_483":26,"_88":744},"_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",[747],{"_247":748,"_482":745,"_483":26,"_88":749},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_247":820,"_482":597,"_483":26,"_88":821},{"_247":805,"_482":819,"_483":124,"_88":806},{"_247":788,"_482":600,"_483":26,"_88":713},{"_247":788,"_482":701,"_483":26,"_88":713},{"_247":817,"_482":597,"_483":26,"_88":818},{"_247":805,"_482":603,"_483":124,"_88":806},{"_247":815,"_482":597,"_483":26,"_88":816},{"_247":805,"_482":600,"_483":124,"_88":806},{"_247":813,"_482":700,"_483":26,"_88":814},{"_247":805,"_482":722,"_483":124,"_88":806},{"_247":785,"_482":600,"_483":26,"_88":787},{"_247":811,"_482":597,"_483":124,"_88":812},{"_247":809,"_482":597,"_483":26,"_88":810},{"_247":807,"_482":597,"_483":124,"_88":808},{"_247":805,"_482":696,"_483":124,"_88":806},{"_247":803,"_482":597,"_483":26,"_88":804},{"_247":801,"_482":597,"_483":26,"_88":802},{"_247":799,"_482":597,"_483":124,"_88":800},{"_247":797,"_482":705,"_483":26,"_88":798},{"_247":785,"_482":696,"_483":26,"_88":787},{"_247":795,"_482":597,"_483":26,"_88":796},{"_247":793,"_482":597,"_483":124,"_88":794},{"_247":791,"_482":597,"_483":26,"_88":792},{"_247":789,"_482":597,"_483":26,"_88":790},{"_247":788,"_482":597,"_483":26,"_88":713},{"_247":785,"_482":786,"_483":26,"_88":787},{"_247":783,"_482":597,"_483":26,"_88":784},{"_247":781,"_482":597,"_483":124,"_88":782},{"_247":779,"_482":597,"_483":26,"_88":780},"_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. 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