Overview

The Dynamo Kubernetes Operator simplifies the deployment, configuration, and lifecycle management of Dynamo inference graphs in Kubernetes environments. It automates the reconciliation of custom resources (CRDs) to ensure your desired state is always achieved, managing health checks, scaling, and component coordination directly through Kubernetes primitives.

Quick Links: Key Components | Release Info | Getting Started | Support

Key Components

DynamoGraph CRD: Defines the complete inference graph topology including frontend, router, planner, and worker configurations.
DynamoGroupDeployment (DGD): Manages groups of worker replicas with coordinated scaling and health monitoring.
DynamoGroupDeploymentReplica (DGDR): Individual worker instance management with optional PVC mounting for profiling.
Validation Webhooks: Validates CRD configurations at submission time, preventing invalid deployments before they start.
Kubernetes-Native Service Discovery: Uses EndpointSlices for service discovery, eliminating etcd dependency in Kubernetes deployments.
Scaling Adapter: Integrates with Planner for SLA-aware autoscaling based on real-time load and latency metrics.\nFor more information about Dynamo Kubernetes deployment, please refer to the GitHub repository and deployment documentation.

Release Info

For the complete release history including architecture details, see the Release Artifacts page.\nPre-built containers are available for both x86_64 (AMD64) and ARM64 architectures.

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.
Install using Helm (recommended):

\nhelm install dynamo-platform oci://helm.ngc.nvidia.com/nvidia/ai-dynamo/charts/dynamo-platform --version \n\">

helm install dynamo-crds oci://helm.ngc.nvidia.com/nvidia/ai-dynamo/charts/dynamo-crds --version <version>\nhelm install dynamo-platform oci://helm.ngc.nvidia.com/nvidia/ai-dynamo/charts/dynamo-platform --version <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 Kubernetes version support.

Related Containers

vLLM Runtime - vLLM inference backend
SGLang Runtime - SGLang inference backend
TensorRT-LLM Runtime - TensorRT-LLM inference backend
Dynamo Frontend - Standalone frontend with EndpointPicker (EPP)

Helm Charts

dynamo-crds: Custom Resource Definitions for DynamoGraph, DGD, and DGDR
dynamo-platform: Operator deployment with optional etcd and NATS\nAvailable from: helm.ngc.nvidia.com/nvidia/ai-dynamo/charts/

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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Dynamo cloud is a kubernetes platform for deploying and managing inference services. This container manages the lifecycle of Dynamo inference deployments in kubernetes.","# Overview\nThe Dynamo Kubernetes Operator simplifies the deployment, configuration, and lifecycle management of Dynamo inference graphs in Kubernetes environments. It automates the reconciliation of custom resources (CRDs) to ensure your desired state is always achieved, managing health checks, scaling, and component coordination directly through Kubernetes primitives.\n\n**Quick Links:** [Key Components](#key-components) | [Release Info](#release-info) | [Getting Started](#getting-started) | [Support](#technical-support)\n\n## Key Components\n- **DynamoGraph CRD**: Defines the complete inference graph topology including frontend, router, planner, and worker configurations.\n- **DynamoGroupDeployment (DGD)**: Manages groups of worker replicas with coordinated scaling and health monitoring.\n- **DynamoGroupDeploymentReplica (DGDR)**: Individual worker instance management with optional PVC mounting for profiling.\n- **Validation Webhooks**: Validates CRD configurations at submission time, preventing invalid deployments before they start.\n- **Kubernetes-Native Service Discovery**: Uses EndpointSlices for service discovery, eliminating etcd dependency in Kubernetes deployments.\n- **Scaling Adapter**: Integrates with Planner for SLA-aware autoscaling based on real-time load and latency metrics.\nFor more information about Dynamo Kubernetes deployment, please refer to the [GitHub repository](https://github.com/ai-dynamo/dynamo) and [deployment documentation](https://docs.nvidia.com/dynamo/latest/kubernetes-deployment/deployment-guide).\n\n## Release Info\nFor the complete release history including 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.\n\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. Install using Helm (recommended):\n```shell\nhelm install dynamo-crds oci://helm.ngc.nvidia.com/nvidia/ai-dynamo/charts/dynamo-crds --version \nhelm install dynamo-platform oci://helm.ngc.nvidia.com/nvidia/ai-dynamo/charts/dynamo-platform --version \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 Kubernetes version support.\n\n## Related Containers\n- [vLLM Runtime](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-dynamo/containers/vllm-runtime) \\- vLLM inference backend\n- [SGLang Runtime](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-dynamo/containers/sglang-runtime) \\- SGLang inference backend\n- [TensorRT-LLM Runtime](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-dynamo/containers/tensorrtllm-runtime) 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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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