dynamo/docs/kubernetes

title
Deployment Guide

High-level guide to Dynamo Kubernetes deployments. Start here, then dive into specific guides.

Important Terminology

Kubernetes Namespace: The K8s namespace where your DynamoGraphDeployment resource is created.

• Used for: Resource isolation, RBAC, organizing deployments
• Example: dynamo-system, team-a-namespace

Dynamo Namespace: The logical namespace used by Dynamo components for service discovery.

• Used for: Runtime component communication, service discovery
• Specified in: .spec.services.<ServiceName>.dynamoNamespace field
• Example: my-llm, production-model, dynamo-dev

These are independent. A single Kubernetes namespace can host multiple Dynamo namespaces, and vice versa.

Prerequisites

Before you begin, ensure you have the following tools installed:

Tool	Minimum Version	Installation Guide
kubectl	v1.24+	Install kubectl
Helm	v3.0+	Install Helm

Verify your installation:

kubectl version --client  # Should show v1.24+
helm version              # Should show v3.0+

For detailed installation instructions, see the Prerequisites section in the Installation Guide.

Pre-deployment Checks

Before deploying the platform, run the pre-deployment checks to ensure the cluster is ready:

./deploy/pre-deployment/pre-deployment-check.sh

This validates kubectl connectivity, StorageClass configuration, and GPU availability. See pre-deployment checks for more details.

1. Install Platform First

# 1. Set environment
export NAMESPACE=dynamo-system
export RELEASE_VERSION=0.x.x # any version of Dynamo 0.3.2+ listed at https://github.com/ai-dynamo/dynamo/releases

# 2. Install Platform (CRDs are automatically installed by the chart)
helm fetch https://helm.ngc.nvidia.com/nvidia/ai-dynamo/charts/dynamo-platform-${RELEASE_VERSION}.tgz
helm install dynamo-platform dynamo-platform-${RELEASE_VERSION}.tgz --namespace ${NAMESPACE} --create-namespace

[!WARNING] v0.9.0 Helm Chart Issue: The initial v0.9.0 dynamo-platform Helm chart sets the operator image to v0.7.1 instead of v0.9.0. Use RELEASE_VERSION=0.9.0-post1 or add --set dynamo-operator.controllerManager.manager.image.tag=0.9.0 to your helm install command.

For Shared/Multi-Tenant Clusters:

If your cluster has namespace-restricted Dynamo operators, add this flag to step 2:

--set dynamo-operator.namespaceRestriction.enabled=true

For more details or customization options (including multinode deployments), see Installation Guide for Dynamo Kubernetes Platform.

2. Choose Your Backend

Each backend has deployment examples and configuration options:

Backend	Aggregated	Aggregated + Router	Disaggregated	Disaggregated + Router	Disaggregated + Planner	Disaggregated Multi-node
SGLang	✅	✅	✅	✅	✅	✅
TensorRT-LLM	✅	✅	✅	✅	🚧	✅
vLLM	✅	✅	✅	✅	✅	✅

3. Deploy Your First Model

Follow the Deploying Your First Model guide for a complete end-to-end walkthrough using DynamoGraphDeploymentRequest (DGDR) — Dynamo's recommended path that handles profiling and configuration automatically.

The tutorial deploys Qwen/Qwen3-0.6B with vLLM and walks you through every step: creating the DGDR, watching the profiling lifecycle, and sending your first inference request.

For SLA-based autoscaling, see SLA Planner Guide.

Understanding Dynamo's Custom Resources

Dynamo provides two main Kubernetes Custom Resources for deploying models:

DynamoGraphDeploymentRequest (DGDR) - Simplified SLA-Driven Configuration

The recommended approach for generating optimal configurations. DGDR provides a high-level interface where you specify:

• Model name and backend framework
• SLA targets (latency requirements)
• GPU type (optional)

Dynamo automatically handles profiling and generates an optimized DGD spec in the status. Perfect for:

• SLA-driven configuration generation
• Automated resource optimization
• Users who want simplicity over control

Note: DGDR generates a DGD spec which you can then use to deploy.

DynamoGraphDeployment (DGD) - Direct Configuration

A lower-level interface that defines your complete inference pipeline:

• Model configuration
• Resource allocation (GPUs, memory)
• Scaling policies
• Frontend/backend connections

Use this when you need fine-grained control or have already completed profiling.

Refer to the API Reference and Documentation for more details.

📖 API Reference & Documentation

For detailed technical specifications of Dynamo's Kubernetes resources:

• API Reference - Complete CRD field specifications for all Dynamo resources
• Create Deployment - Step-by-step deployment creation with DynamoGraphDeployment
• Operator Guide - Dynamo operator configuration and management

Choosing Your Architecture Pattern

When creating a deployment, select the architecture pattern that best fits your use case:

• Development / Testing - Use agg.yaml as the base configuration
• Production with Load Balancing - Use agg_router.yaml to enable scalable, load-balanced inference
• High Performance / Disaggregated - Use disagg_router.yaml for maximum throughput and modular scalability

Frontend and Worker Components

You can run the Frontend on one machine (e.g., a CPU node) and workers on different machines (GPU nodes). The Frontend serves as a framework-agnostic HTTP entry point that:

• Provides OpenAI-compatible /v1/chat/completions endpoint
• Auto-discovers backend workers via service discovery (Kubernetes-native by default)
• Routes requests and handles load balancing
• Validates and preprocesses requests

Customizing Your Deployment

Example structure:

apiVersion: nvidia.com/v1alpha1
kind: DynamoGraphDeployment
metadata:
  name: my-llm
spec:
  services:
    Frontend:
      dynamoNamespace: my-llm
      componentType: frontend
      replicas: 1
      extraPodSpec:
        mainContainer:
          image: your-image
    VllmDecodeWorker:  # or SGLangDecodeWorker, TrtllmDecodeWorker
      dynamoNamespace: dynamo-dev
      componentType: worker
      replicas: 1
      envFromSecret: hf-token-secret  # for HuggingFace models
      resources:
        limits:
          gpu: "1"
      extraPodSpec:
        mainContainer:
          image: your-image
          command: ["/bin/sh", "-c"]
          args:
            - python3 -m dynamo.vllm --model YOUR_MODEL [--your-flags]

Worker command examples per backend:

# vLLM worker
args:
  - python3 -m dynamo.vllm --model Qwen/Qwen3-0.6B

# SGLang worker
args:
  - >-
    python3 -m dynamo.sglang
    --model-path deepseek-ai/DeepSeek-R1-Distill-Llama-8B
    --tp 1
    --trust-remote-code    

# TensorRT-LLM worker
args:
  - python3 -m dynamo.trtllm
    --model-path deepseek-ai/DeepSeek-R1-Distill-Llama-8B
    --served-model-name deepseek-ai/DeepSeek-R1-Distill-Llama-8B
    --extra-engine-args /workspace/examples/backends/trtllm/engine_configs/deepseek-r1-distill-llama-8b/agg.yaml

Key customization points include:

• Model Configuration: Specify model in the args command
• Resource Allocation: Configure GPU requirements under resources.limits
• Scaling: Set replicas for number of worker instances
• Routing Mode: Enable KV-cache routing by setting DYN_ROUTER_MODE=kv in Frontend envs
• Worker Specialization: Add --disaggregation-mode prefill flag for disaggregated prefill workers

Additional Resources

• Examples - Complete working examples
• Create Custom Deployments - Build your own CRDs
• Managing Models with DynamoModel - Deploy LoRA adapters and manage models
• Operator Documentation - How the platform works
• Service Discovery - Discovery backends and configuration
• Helm Charts - For advanced users
• Snapshot - Fast pod startup with checkpoint/restore
• GitOps Deployment with FluxCD - For advanced users
• Logging - For logging setup
• Multinode Deployment - For multinode deployment
• Grove - For grove details and custom installation
• Monitoring - For monitoring setup
• Model Caching with Fluid - For model caching with Fluid