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+# NVIDIA NIM on GKE
+
+## Before you begin
+
+1. Get access to NVIDIA NIMs
+> [!IMPORTANT]
+> Before you proceed further, ensure you have the NVIDIA AI Enterprise License (NVAIE) to access the NIMs.  To get started, go to [build.nvidia.com](https://build.nvidia.com/explore/discover?signin=true) and provide your company email address
+
+2. In the [Google Cloud console](https://console.cloud.google.com), on the project selector page, select or create a new project with [billing enabled](https://cloud.google.com/billing/docs/how-to/verify-billing-enabled#console)
+
+3. Ensure you have the following tools installed on your workstation
+* [gcloud CLI](https://cloud.google.com/sdk/docs/install)
+* [gcloud kubectl](https://cloud.google.com/kubernetes-engine/docs/how-to/cluster-access-for-kubectl#install_kubectl)
+* [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
+* [jq](https://jqlang.github.io/jq/)
+* [ngc](https://ngc.nvidia.com/setup)
+
+4. Enable the required APIs
+```bash
+gcloud services enable \
+  container.googleapis.com \
+  file.googleapis.com
+```
+
+## Set up your GKE Cluster
+
+1. Choose your region and set your project and machine variables:
+```bash
+export PROJECT_ID=$(gcloud config get project)
+export REGION=us-central1
+export ZONE=${REGION?}-b
+export MACH=a2-highgpu-1g
+export GPU_TYPE=nvidia-tesla-a100
+export GPU_COUNT=1
+```
+
+
+2. Create a GKE cluster:
+```bash
+gcloud container clusters create nim-demo --location ${REGION?} \
+  --workload-pool ${PROJECT_ID?}.svc.id.goog \
+  --enable-image-streaming \
+  --enable-ip-alias \
+  --node-locations ${ZONE?} \
+  --workload-pool=${PROJECT_ID?}.svc.id.goog \
+  --addons=GcpFilestoreCsiDriver  \
+  --machine-type n2d-standard-4 \
+  --num-nodes 1 --min-nodes 1 --max-nodes 5 \
+  --ephemeral-storage-local-ssd=count=2
+```
+
+3. Create a nodepool
+```bash
+gcloud container node-pools create ${MACH?}-node-pool --cluster nim-demo \
+   --accelerator type=${GPU_TYPE?},count=${GPU_COUNT?},gpu-driver-version=latest \
+  --machine-type ${MACH?} \
+  --ephemeral-storage-local-ssd=count=${GPU_COUNT?} \
+  --enable-autoscaling --enable-image-streaming \
+  --num-nodes=1 --min-nodes=1 --max-nodes=3 \
+  --node-locations ${ZONE?} \
+  --region ${REGION?} \
+  --spot
+```
+
+
+## Set Up Access to NVIDIA NIMs and prepare environment
+
+1. Get your NGC_API_KEY from NGC
+```bash
+export NGC_CLI_API_KEY="<YOUR_API_KEY>"
+```
+> [!NOTE]
+> If you have not set up NGC, see [NGC Setup](https://ngc.nvidia.com/setup) to get your access key and begin using NGC.
+
+2. As a part of the NGC setup, set your configs
+```bash
+ngc config set
+```
+
+3. Ensure you have access to the repository by listing the models
+```bash
+ngc registry model list
+```
+
+4. Create a Kuberntes namespace
+```bash
+kubectl create namespace nim
+```
+
+## Deploy a PVC to persist the model
+1. Create a PVC to persist the model weights - recommended for deployments with more than one (1) replica.  Save the following yaml as `pvc.yaml`.
+```yaml
+apiVersion: v1
+kind: PersistentVolumeClaim
+metadata:
+  name: model-store-pvc
+  namespace: nim
+spec:
+  accessModes:
+    - ReadWriteMany
+  resources:
+    requests:
+      storage: 30Gi
+  storageClassName: standard-rwx
+```
+
+2. Apply PVC
+```bash
+kubectl apply -f pvc.yaml
+```
+> [!NOTE]
+> This PVC will [dynamically provision a PV](https://cloud.google.com/kubernetes-engine/docs/concepts/persistent-volumes#dynamic_provisioning) with the necessary storage to persist model weights across replicas of your pods.
+
+## Deploy the NIM with the generated engine using a Helm chart
+
+1. Clone the nim-deploy repository
+```bash
+git clone https://github.com/NVIDIA/nim-deploy.git
+cd nim-deploy/helm
+```
+
+2. Deploy chart with minimal configurations
+```bash
+helm --namespace nim install demo-nim nim-llm/ --set model.ngcAPIKey=$NGC_CLI_API_KEY --set persistence.enabled=true --set persistence.existingClaim=model-store-pvc
+```
+
+## Test the NIM
+1. Expose the service
+```bash
+kubectl port-forward --namespace nim services/demo-nim-nim-llm 8000
+```
+
+2. Send a test prompt - A100
+```bash
+curl -X 'POST' \
+  'http://localhost:8000/v1/chat/completions' \
+  -H 'accept: application/json' \
+  -H 'Content-Type: application/json' \
+  -d '{
+  "messages": [
+    {
+      "content": "You are a polite and respectful poet.",
+      "role": "system"
+    },
+    {
+      "content": "Write a limerick about the wonders of GPUs and Kubernetes?",
+      "role": "user"
+    }
+  ],
+  "model": "meta/llama3-8b-instruct",
+  "max_tokens": 256,
+  "top_p": 1,
+  "n": 1,
+  "stream": false,
+  "frequency_penalty": 0.0
+}' | jq '.choices[0].message.content' -
+```
+
+3. Browse the API by navigating to http://localhost:8000/docs
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