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",1785175152201],[180,261,259,257,255,253,251,249,247,245,242],"/api/image/nvidia/-/containers/sglang",9460925082,"hasSignedTag",[],[290,291,292,293,294,295,296,297,298,299,300,301],[],[282],[],{"_91":283,"_284":283,"_285":286,"_287":288},"signed images","value","filter",{"_289":288,"_284":283},"category","system","field",{"_91":337,"_284":338,"_285":339,"_287":305},{"_91":334,"_284":335,"_285":336,"_287":305},{"_91":331,"_284":332,"_285":333,"_287":305},{"_91":329,"_284":329,"_285":330,"_287":305},{"_91":326,"_284":327,"_285":328,"_287":305},{"_91":323,"_284":324,"_285":325,"_287":305},{"_91":320,"_284":321,"_285":322,"_287":305},{"_91":317,"_284":318,"_285":319,"_287":305},{"_91":314,"_284":315,"_285":316,"_287":305},{"_91":310,"_284":311,"_285":312,"_287":305},{"_91":307,"_284":308,"_285":309,"_287":305},{"_91":302,"_284":303,"_285":304,"_287":305},"Translation","uscs_translation",{"_289":306,"_284":303},"label","application","Question Answering","uscs_question_answering",{"_289":306,"_284":308},"NVIDIA AI","soln_nvidia_ai",{"_289":313,"_284":311},"technology","Natural Language Understanding","uscs_natural_language_understanding",{"_289":306,"_284":315},"Natural Language Processing","uscs_natural_language_processing",{"_289":306,"_284":318},"ML","soln_ml",{"_289":313,"_284":321},"Inference","soln_inference",{"_289":313,"_284":324},"High Performance Computing","uscs_high_performance_computing",{"_289":306,"_284":327},"DLFW",{"_289":305,"_284":329},"DL","soln_dl",{"_289":313,"_284":332},"Conversational AI","soln_conversational_ai",{"_289":313,"_284":335},"AI","soln_ai",{"_289":313,"_284":338},{"_147":1314,"_149":1315},"ngcApps",[1267],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1248":1249,"_1250":1251,"_1252":1253,"_1254":1255,"_1256":351,"_179":377,"_1257":1258,"_1259":51,"_1260":126,"_1261":126,"_1262":1263,"_1264":126,"_1265":1266},"amplitude",{"_1245":1246,"_890":-7,"_1247":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1239":1240,"_1241":1242,"_1243":1244},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787172932879,"1.12.0","defaultSearchSuggestions",{"_1130":104,"_1131":1132,"_1133":1134,"_1135":1136},"announcements",[],"consentState",{"_416":1120,"_1121":126,"_1122":26,"_1123":26,"_1124":1125},"consentConfig",{"_398":399,"_400":401,"_402":403,"_404":405,"_406":407,"_408":409,"_410":411,"_412":413,"_414":415,"_416":417},"showContactAdmin",1787172932880,[],[],[391,392,393,394,395,396,397],{"_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":54,"_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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These cookies may store a unique ID so that our system will remember you when you return. 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