Dynamo Deployment on GKE

Pre-requisites

Install gcloud CLI

https://cloud.google.com/sdk/docs/install

Create GKE cluster

$ export PROJECT_ID=<>
$ export REGION=<>
$ export ZONE=<>
$ export CLUSTER_NAME=<>
$ export CLUSTER_MACHINE_TYPE=n2-standard-4
$ export NODE_POOL_MACHINE_TYPE=g2-standard-24
$ export GPU_TYPE=nvidia-l4
$ export GPU_COUNT=2
$ export CPU_NODE=2
$ export GPU_NODE=2
$ export DISK_SIZE=200
$ 
$ gcloud container clusters create ${CLUSTER_NAME} \
>  	--project=${PROJECT_ID} \
>  	--location=${ZONE} \
> 	--subnetwork=default \
>     --disk-size=${DISK_SIZE} \
> 	--machine-type=${CLUSTER_MACHINE_TYPE} \
>  	--num-nodes=${CPU_NODE}

Create GPU pool

$ gcloud container node-pools create gpu-pool \
>  	--accelerator type=${GPU_TYPE},count=${GPU_COUNT},gpu-driver-version=latest \
>  	--project=${PROJECT_ID} \
>  	--location=${ZONE} \
>  	--cluster=${CLUSTER_NAME} \
> 	--machine-type=${NODE_POOL_MACHINE_TYPE} \
>     --disk-size=${DISK_SIZE} \
>     --num-nodes=${GPU_NODE} \
>     --enable-autoscaling \
>     --min-nodes=1 \
>     --max-nodes=3

Clone Dynamo GitHub repository

Note: Please make sure GitHub branch/commit version matches with Dynamo platform and VLLM container.

$ git clone https://github.com/ai-dynamo/dynamo.git
$ 
$ # Checkout to the desired branch
$ git checkout release/0.6.0

Set environment variables for GKE

$ export NAMESPACE=dynamo-system
$ kubectl create namespace $NAMESPACE
$ kubectl config set-context --current --namespace=$NAMESPACE
$ 
$ export HF_TOKEN=<HF_TOKEN>
$ kubectl create secret generic hf-token-secret \
>   --from-literal=HF_TOKEN=${HF_TOKEN} \
>   -n ${NAMESPACE}

Install Dynamo Kubernetes Platform

See installation steps

After installation, verify the installation:

Expected output

$ kubectl get pods
$ NAME                                                              READY   STATUS             RESTARTS   AGE
$ dynamo-platform-dynamo-operator-controller-manager-69b9794fpgv9   2/2     Running            0          4m27s
$ dynamo-platform-etcd-0                                            1/1     Running            0          4m27s
$ dynamo-platform-nats-0                                            2/2     Running            0          4m27s

Deploy Inference Graph

We will deploy a LLM model to the Dynamo platform. Here we use Qwen/Qwen3-0.6B model with VLLM and disaggregated deployment as an example.

In the deployment yaml file, some adjustments have to/ could be made:

(Required) Add args to change LD_LIBRARY_PATH and PATH of decoder container, to enable GKE find the correct GPU driver
Change VLLM image to the desired one on NGC
Add namespace to metadata
Adjust GPU/CPU request and limits
Change model to deploy

More configurations please refer to https://github.com/ai-dynamo/dynamo/tree/main/examples/deployments/GKE/vllm

Highlighted configurations in yaml file

Please note that LD_LIBRARY_PATH needs to be set properly in GKE as per Run GPUs in GKE

The following snippet needs to be present in the args field of the deployment yaml file:

$ export LD_LIBRARY_PATH=/usr/local/nvidia/lib64:$LD_LIBRARY_PATH
$ export PATH=$PATH:/usr/local/nvidia/bin:/usr/local/nvidia/lib64
$ /sbin/ldconfig

For example, refer to the following from examples/deployments/GKE/vllm/disagg.yaml

1 metadata:
2   name: vllm-disagg
3   namespace: dynamo-system
4 spec:
5   services:
6     Frontend:
7           image: nvcr.io/nvidia/ai-dynamo/vllm-runtime:0.6.0
8     VllmDecodeWorker:
9       resources:
10         limits:
11           gpu: "3"
12           image: nvcr.io/nvidia/ai-dynamo/vllm-runtime:0.6.0
13           args:
14             - |
15             export LD_LIBRARY_PATH=/usr/local/nvidia/lib64:$LD_LIBRARY_PATH
16             export PATH=$PATH:/usr/local/nvidia/bin:/usr/local/nvidia/lib64
17             /sbin/ldconfig
18             python3 -m dynamo.vllm --model Qwen/Qwen3-0.6B

Deploy the model

$ cd dynamo/examples/deployments/GKE/vllm
$ 
$ kubectl apply -f disagg_gke.yaml -n ${NAMESPACE}

Expected output after successful deployment

$ kubectl get pods
$ NAME                                                              READY   STATUS    RESTARTS   AGE
$ dynamo-platform-dynamo-operator-controller-manager-c665684ssqkx   2/2     Running   0          65m
$ dynamo-platform-etcd-0                                            1/1     Running   0          65m
$ dynamo-platform-nats-0                                            2/2     Running   0          65m
$ vllm-disagg-frontend-5954ddc4dd-4w2cb                             1/1     Running   0          11m
$ vllm-disagg-vllmdecodeworker-77844cfcff-ddn4v                     1/1     Running   0          11m
$ vllm-disagg-vllmprefillworker-55d5b74b4f-zrskh                    1/1     Running   0          11m

Test the Deployment

$ export DEPLOYMENT_NAME=vllm-disagg
$ 
$ # Find the frontend pod
$ export FRONTEND_POD=$(kubectl get pods -n ${NAMESPACE} | grep "${DEPLOYMENT_NAME}-frontend" | sort -k1 | tail -n1 | awk '{print $1}')
$ 
$ # Forward the pod's port to localhost
> kubectl port-forward deployment/vllm-disagg-frontend  8000:8000 -n ${NAMESPACE}
> 
> # disagg
> curl localhost:8000/v1/chat/completions \
>   -H "Content-Type: application/json" \
>   -d '{
>     "model": "Qwen/Qwen3-0.6B",
>     "messages": [
>     {
>         "role": "user",
>         "content": "In the heart of Eldoria, an ancient land of boundless magic and mysterious creatures, lies the long-forgotten city of Aeloria. Once a beacon of knowledge and power, Aeloria was buried beneath the shifting sands of time, lost to the world for centuries. You are an intrepid explorer, known for your unparalleled curiosity and courage, who has stumbled upon an ancient map hinting at ests that Aeloria holds a secret so profound that it has the potential to reshape the very fabric of reality. Your journey will take you through treacherous deserts, enchanted forests, and across perilous mountain ranges. Your Task: Character Background: Develop a detailed background for your character. Describe their motivations for seeking out Aeloria, their skills and weaknesses, and any personal connections to the ancient city or its legends. Are they driven by a quest for knowledge, a search for lost familt clue is hidden."
>     }
>     ],
>     "stream":false,
>     "max_tokens": 30
>   }'

Response

1 {"id":"chatcmpl-bd0670d9-0342-4eea-97c1-99b69f1f931f","choices":[{"index":0,"message":{"content":"Okay, here's a detailed character background for your intrepid explorer, tailored to fit the premise of Aeloria, with a focus on a","refusal":null,"tool_calls":null,"role":"assistant","function_call":null,"audio":null},"finish_reason":"stop","logprobs":null}],"created":1756336263,"model":"Qwen/Qwen3-0.6B","service_tier":null,"system_fingerprint":null,"object":"chat.completion","usage":{"prompt_tokens":190,"completion_tokens":29,"total_tokens":219,"prompt_tokens_details":null,"completion_tokens_details":null}}