Overview

The Dynamo SGLang runtime container is a pre-packaged, Docker-based environment tailored for running NVIDIA Dynamo with the SGLang backend for high-performance, modular large language model (LLM) inference and serving. It packages all necessary dependencies, runtime components, and optimizations to streamline deployment and ensure consistency across development and production environments.\nQuick Links: Key Components | Release Info | Getting Started | Support

Key Components

SGLang Backend: Fast serving framework for large language models and vision language models with co-designed backend runtime and frontend language for faster, more controllable model interaction.
Disaggregated Serving (P/D): Separates prefill and decode phases across specialized workers for improved throughput and latency optimization.
Planner: SLA-aware request scheduling that routes requests based on latency targets and system load.
KV Router: Intelligent request routing with prefix-aware caching to maximize KV cache reuse across workers.
NIXL (KV Transfer Library): High-performance GPU-to-GPU memory transfer for distributed KV cache operations.
OpenAI-Compatible Frontend: HTTP API server compatible with OpenAI's chat completions and completions endpoints.
Kubernetes-Native Infrastructure: Service discovery via EndpointSlices and transport-agnostic request plane (TCP default) enable deployment without external dependencies. etcd and NATS remain available as optional alternatives for non-Kubernetes environments.\nFor more information about Dynamo features, please refer to the GitHub repository and documentation.

Release Info

For the complete release history including SGLang versions, CUDA support, and architecture details, see the Release Artifacts page.\nPre-built containers are available for both x86_64 (AMD64) and ARM64 architectures. CUDA 13 experimental variants are also available.

Getting Started

Select the Tags tab and locate the container image release that you want to run.
In the Pull Tag column, click the icon to copy the docker pull command.
Open a command prompt and paste the pull command. Ensure the pull completes successfully.
Run the container:

\n\">

docker run --gpus all -it nvcr.io/nvidia/ai-dynamo/sglang-runtime:<version>\n

For next steps, including deployment options and examples, please refer to the Dynamo README.

Support Matrix

Please refer to the support matrix for detailed hardware, architecture, and model support information.

Related Containers

vLLM Runtime - Broadest model and feature coverage
TensorRT-LLM Runtime - Maximum inference performance
Dynamo Frontend - Standalone frontend with EndpointPicker (EPP)
Kubernetes Operator - K8s deployment automation

License

NVIDIA Dynamo is released under the Apache-2.0 open-source license, making it freely available for development, research, and deployment.

Technical Support

Documentation: Dynamo Documentation
GitHub Issues: Dynamo GitHub Issues
Release Notes: GitHub Releases

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It packages all necessary dependencies, runtime components, and optimizations to streamline deployment and ensure consistency across development and production environments.\n**Quick Links:** [Key Components](#key-components) | [Release Info](#release-info) | [Getting Started](#getting-started) | [Support](#technical-support)\n## Key Components\n- [**SGLang**](https://github.com/sgl-project/sglang) **Backend**: Fast serving framework for large language models and vision language models with co-designed backend runtime and frontend language for faster, more controllable model interaction.\n- **Disaggregated Serving (P/D)**: Separates prefill and decode phases across specialized workers for improved throughput and latency optimization.\n- **Planner**: SLA-aware request scheduling that routes requests based on latency targets and system load.\n- **KV Router**: Intelligent request routing with prefix-aware caching to maximize KV cache reuse across workers.\n- **NIXL (KV Transfer Library)**: High-performance GPU-to-GPU memory transfer for distributed KV cache operations.\n- **OpenAI-Compatible Frontend**: HTTP API server compatible with OpenAI's chat completions and completions endpoints.\n- **Kubernetes-Native Infrastructure**: Service discovery via EndpointSlices and transport-agnostic request plane (TCP default) enable deployment without external dependencies. etcd and NATS remain available as optional alternatives for non-Kubernetes environments.\nFor more information about Dynamo features, please refer to the [GitHub repository](https://github.com/ai-dynamo/dynamo) and [documentation](https://docs.nvidia.com/dynamo/latest/).\n## Release Info\nFor the complete release history including SGLang versions, CUDA support, and architecture details, see the [Release Artifacts](https://docs.nvidia.com/dynamo/latest/getting-started/release-artifacts) page.\nPre-built containers are available for both x86_64 (AMD64) and ARM64 architectures. CUDA 13 experimental variants are also available.\n## Getting Started\n1. Select the **Tags** tab and locate the container image release that you want to run.\n2. In the **Pull Tag** column, click the icon to copy the docker pull command.\n3. Open a command prompt and paste the pull command. Ensure the pull completes successfully.\n4. Run the container:\n```shell\ndocker run --gpus all -it nvcr.io/nvidia/ai-dynamo/sglang-runtime:\n```\nFor next steps, including deployment options and examples, please refer to the [Dynamo README](https://github.com/ai-dynamo/dynamo#readme).\n## Support Matrix\nPlease refer to the [support matrix](https://docs.nvidia.com/dynamo/latest/getting-started/support-matrix) for detailed hardware, architecture, and model support information.\n## Related Containers\n- [vLLM Runtime](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-dynamo/containers/vllm-runtime) \\- Broadest model and feature coverage\n- [TensorRT-LLM Runtime](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-dynamo/containers/tensorrtllm-runtime) \\- Maximum inference performance\n- [Dynamo Frontend](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-dynamo/containers/dynamo-frontend) \\- Standalone frontend with EndpointPicker (EPP)\n- [Kubernetes Operator](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-dynamo/containers/kubernetes-operator) \\- K8s deployment automation\n## License\nNVIDIA Dynamo is released under the Apache-2.0 open-source license, making it freely available for development, research, and deployment.\n## Technical Support\n- **Documentation**: [Dynamo Documentation](https://docs.nvidia.com/dynamo/latest/)\n- **GitHub Issues**: [Dynamo GitHub Issues](https://github.com/ai-dynamo/dynamo/issues)\n- **Release Notes**: [GitHub Releases](https://github.com/ai-dynamo/dynamo/releases)",["D",1784310046949],[195,299,297,295,293,291,289,301,287,283,281,279,277,275,273,271,269,267,264,285],"/api/image/nvidia/ai-dynamo/containers/sglang-runtime-nightly",12784084001,"hasSignedTag",[],[325,326,327,328,329,330,331,332],[],[321],[],{"_92":322,"_126":322,"_127":323,"_129":324},"signed images",{"_131":324,"_126":322},"system",{"_92":354,"_126":355,"_127":356,"_129":130},{"_92":351,"_126":352,"_127":353,"_129":130},{"_92":347,"_126":348,"_127":349,"_129":130},{"_92":344,"_126":345,"_127":346,"_129":130},{"_92":341,"_126":342,"_127":343,"_129":130},{"_92":338,"_126":339,"_127":340,"_129":130},{"_92":132,"_126":132,"_127":337,"_129":130},{"_92":333,"_126":334,"_127":335,"_129":130},"NVIDIA AI","soln_nvidia_ai",{"_131":336,"_126":334},"technology",{"_131":130,"_126":132},"ML","soln_ml",{"_131":336,"_126":339},"Infrastructure Software","soln_infrastructure_software",{"_131":336,"_126":342},"Inference","soln_inference",{"_131":336,"_126":345},"High Performance Computing","uscs_high_performance_computing",{"_131":350,"_126":348},"application","DL","soln_dl",{"_131":336,"_126":352},"AI","soln_ai",{"_131":336,"_126":355},{"_162":1529,"_164":1530},"ngcApps",[1482],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1463":1464,"_1465":1466,"_1467":1468,"_1469":1470,"_1471":368,"_194":392,"_1472":1473,"_1474":51,"_1475":141,"_1476":141,"_1477":1478,"_1479":141,"_1480":1481},"amplitude",{"_1460":1461,"_1066":-7,"_1462":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1454":1455,"_1456":1457,"_1458":1459},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1341":473,"_1342":1343,"_1344":1345,"_1346":1347},"announcements",[],"consentState",{"_430":1331,"_1332":141,"_1333":26,"_1334":26,"_1335":1336},"consentConfig",{"_412":413,"_414":415,"_416":417,"_418":419,"_420":421,"_422":423,"_424":425,"_426":427,"_428":429,"_430":431},"showContactAdmin",1784329079416,[],[],[406,407,408,409,410,411],{"_40":62,"_42":67,"_44":157,"_46":-5,"_48":-5,"_50":51},{"_40":62,"_42":65,"_44":157,"_46":-5,"_48":-5,"_50":51},{"_40":62,"_42":63,"_44":157,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":157,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":157,"_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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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.","research.nvidia.com","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","s_pltp","This cookie is typically associated with Adobe Analytics (formerly SiteCatalyst). It is used to identify the previous link clicked within the site, which helps in analyzing site usage patterns.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","This cookie name is used as a long-lived unique identifier of the visitor on the website. 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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. 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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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