Deploying Dynamo on Kubernetes#

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, dynamo-cloud, team-a-namespace

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

  • 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.

Pre-deployment Checks#

Before deploying the platform, it is recommended to run the pre-deployment checks to ensure the cluster is ready for deployment. Please refer to the 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 CRDs (skip if on shared cluster where CRDs already exist)
helm fetch https://helm.ngc.nvidia.com/nvidia/ai-dynamo/charts/dynamo-crds-${RELEASE_VERSION}.tgz
helm install dynamo-crds dynamo-crds-${RELEASE_VERSION}.tgz --namespace default

# 3. Install Platform
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

For Shared/Multi-Tenant Clusters:

If your cluster has namespace-restricted Dynamo operators, add this flag to step 3:

--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#

export NAMESPACE=dynamo-system
kubectl create namespace ${NAMESPACE}

# to pull model from HF
export HF_TOKEN=<Token-Here>
kubectl create secret generic hf-token-secret \
  --from-literal=HF_TOKEN="$HF_TOKEN" \
  -n ${NAMESPACE};

# Deploy any example (this uses vLLM with Qwen model using aggregated serving)
kubectl apply -f components/backends/vllm/deploy/agg.yaml -n ${NAMESPACE}

# Check status
kubectl get dynamoGraphDeployment -n ${NAMESPACE}

# Test it
kubectl port-forward svc/vllm-agg-frontend 8000:8000 -n ${NAMESPACE}
curl http://localhost:8000/v1/models

For SLA-based autoscaling, see SLA Planner Quick Start Guide.

What’s a DynamoGraphDeployment (DGD)?#

docs: address Harry/VDR feedback + fixing broken links across repository (#3802))

It’s a Kubernetes Custom Resource that defines your inference pipeline:

  • Model configuration

  • Resource allocation (GPUs, memory)

  • Scaling policies

  • Frontend/backend connections

Refer to the API Reference and Documentation for more details.

📖 API Reference & Documentation#

For detailed technical specifications of Dynamo’s Kubernetes resources:

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 etcd

  • 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 engine_configs/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 --is-prefill-worker flag for disaggregated prefill workers

Additional Resources#