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",1782490432029],[253,251,249,247,245,175,242,240,238,235],"/api/image/nvidia/-/containers/sglang",9112473084,"hasSignedTag",[],[281,282,283,284,285,286,287,288,289,290,291,292],[],[273],[],{"_91":274,"_275":274,"_276":277,"_278":279},"signed images","value","filter",{"_280":279,"_275":274},"category","system","field",{"_91":328,"_275":329,"_276":330,"_278":296},{"_91":325,"_275":326,"_276":327,"_278":296},{"_91":322,"_275":323,"_276":324,"_278":296},{"_91":320,"_275":320,"_276":321,"_278":296},{"_91":317,"_275":318,"_276":319,"_278":296},{"_91":314,"_275":315,"_276":316,"_278":296},{"_91":311,"_275":312,"_276":313,"_278":296},{"_91":308,"_275":309,"_276":310,"_278":296},{"_91":305,"_275":306,"_276":307,"_278":296},{"_91":301,"_275":302,"_276":303,"_278":296},{"_91":298,"_275":299,"_276":300,"_278":296},{"_91":293,"_275":294,"_276":295,"_278":296},"Translation","uscs_translation",{"_280":297,"_275":294},"label","application","Question Answering","uscs_question_answering",{"_280":297,"_275":299},"NVIDIA AI","soln_nvidia_ai",{"_280":304,"_275":302},"technology","Natural Language Understanding","uscs_natural_language_understanding",{"_280":297,"_275":306},"Natural Language Processing","uscs_natural_language_processing",{"_280":297,"_275":309},"ML","soln_ml",{"_280":304,"_275":312},"Inference","soln_inference",{"_280":304,"_275":315},"High Performance Computing","uscs_high_performance_computing",{"_280":297,"_275":318},"DLFW",{"_280":296,"_275":320},"DL","soln_dl",{"_280":304,"_275":323},"Conversational AI","soln_conversational_ai",{"_280":304,"_275":326},"AI","soln_ai",{"_280":304,"_275":329},{"_146":1502,"_148":1503},"ngcApps",[1455],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1437":1438,"_1439":1440,"_1441":1442,"_1443":1444,"_1445":342,"_174":366,"_1446":1447,"_1435":51,"_1448":126,"_1449":126,"_1450":1451,"_1452":126,"_1453":1454},"amplitude",{"_1433":1434,"_1435":51,"_1040":-7,"_1436":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1427":1428,"_1429":1430,"_1431":1432},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.4.0","defaultSearchSuggestions",{"_1315":104,"_1316":1317,"_1318":1319,"_1320":1321},"announcements",[],"consentState",{"_405":1305,"_1306":126,"_1307":26,"_1308":26,"_1309":1310},"consentConfig",{"_387":388,"_389":390,"_391":392,"_393":394,"_395":396,"_397":398,"_399":400,"_401":402,"_403":404,"_405":406},"showContactAdmin",1783362690406,[],[],[380,381,382,383,384,385],{"_40":61,"_42":66,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":61,"_42":64,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":61,"_42":62,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":59,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":386,"_50":51},3,"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.",5,[556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590],[429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445],{"_446":552,"_91":552,"_90":104,"_419":553},{"_446":547,"_91":547,"_90":104,"_419":548},{"_446":543,"_91":543,"_90":104,"_419":544},{"_446":538,"_91":538,"_90":104,"_419":539},{"_446":533,"_91":533,"_90":104,"_419":534},{"_446":521,"_91":521,"_90":104,"_419":522},{"_446":509,"_91":509,"_90":104,"_419":510},{"_446":504,"_91":504,"_90":104,"_419":505},{"_446":493,"_91":493,"_90":104,"_419":494},{"_446":488,"_91":488,"_90":104,"_419":489},{"_446":483,"_91":483,"_90":104,"_419":484},{"_446":478,"_91":478,"_90":104,"_419":479},{"_446":473,"_91":473,"_90":104,"_419":474},{"_446":468,"_91":468,"_90":104,"_419":469},{"_446":463,"_91":463,"_90":104,"_419":464},{"_446":456,"_91":456,"_90":104,"_419":457},{"_446":447,"_91":447,"_90":104,"_419":448},"hostName","amazon-adsystem.com",[449,450],{"_236":455,"_452":447,"_453":26,"_90":454},{"_236":451,"_452":447,"_453":26,"_90":454},"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",[458,459],{"_236":462,"_452":456,"_453":26,"_90":461},{"_236":460,"_452":456,"_453":26,"_90":461},"IDE","This domain is owned by Doubleclick (Google). 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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. 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This function remembers how far the user has scrolled on the current session across the website's sub-pages.","dmn_chk_-xxxxxxxxxxxx","com","Posthog Analytics","_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.","_cs_id","blogs.nvidia.com","_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.","_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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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. 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.","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","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","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.","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.","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.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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","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.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","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.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","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","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.","session","This cookie is typically used to maintain an anonymized user session by the server.","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.","AWS ELB application load balancer","This cookie is associated with Akamai. It is used to analyse traffic to determine if it is automated traffic generated by bots or a human user.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","login_session","bm_ss","This cookie is used to help protect websites from automated bot traffic.","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.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. 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