docs: add 5-minute quick start path (#412)

docs/get-started/quick-start.md

@@ -0,0 +1,126 @@
+---
+title: 5-Minute Quick Start
+sidebar_label: Quick Start
+---
+
+This guide gets HAMi running on an existing Kubernetes cluster with at least one NVIDIA GPU node in under 5 minutes. It uses a single Helm command for installation and one test pod to confirm GPU sharing works.
+
+For production setups, offline installs, or non-NVIDIA devices, see the [Installation](../installation/online-installation) section.
+
+## Prerequisites
+
+- A running Kubernetes cluster (v1.18+)
+- At least one GPU node with NVIDIA drivers (v440+) and `nvidia-container-toolkit` installed and set as the default container runtime
+- Helm v3+
+- `kubectl` configured to reach the cluster
+
+:::note
+The node runtime must already be configured before installing HAMi. If it is not, follow the [Prerequisites](../installation/prerequisites) guide first, then return here.
+:::
+
+## Step 1 - Install HAMi
+
+Add the HAMi Helm repository:
+
+```bash
+helm repo add hami-charts https://project-hami.github.io/HAMi/
+helm repo update
+```
+
+Get your Kubernetes server version:
+
+```bash
+kubectl version
+```
+
+Look for the `Server Version` line in the output. Install HAMi, replacing `v1.XX.X` with that version:
+
+```bash
+helm install hami hami-charts/hami \
+  --set scheduler.kubeScheduler.imageTag=v1.XX.X \
+  -n kube-system
+```
+
+Label each GPU node so HAMi can schedule workloads onto it. Replace `<node-name>` with the output of `kubectl get nodes`:
+
+```bash
+kubectl label nodes <node-name> gpu=on
+```
+
+## Step 2 - Verify HAMi is running
+
+```bash
+kubectl get pods -n kube-system | grep hami
+```
+
+Expected output:
+
+```text
+hami-device-plugin-<hash>   1/1     Running   0   1m
+hami-scheduler-<hash>       1/1     Running   0   1m
+```
+
+Both pods must reach `Running` state before submitting workloads. If they do not, check the [Validate HAMi](./verify-hami) guide for troubleshooting steps.
+
+## Step 3 - Run a GPU sharing example
+
+The following pod requests one virtual GPU with a 4096 MiB memory cap. Two such pods can share a single physical GPU simultaneously.
+
+```bash
+kubectl apply -f - <<EOF
+apiVersion: v1
+kind: Pod
+metadata:
+  name: hami-quick-test
+spec:
+  containers:
+    - name: test
+      image: ubuntu:22.04
+      command: ["bash", "-c", "sleep 86400"]
+      resources:
+        limits:
+          nvidia.com/gpu: 1
+          nvidia.com/gpumem: 4096
+EOF
+```
+
+Wait for the pod to be ready:
+
+```bash
+kubectl wait --for=condition=Ready pod/hami-quick-test --timeout=120s
+```
+
+## Step 4 - Confirm memory isolation
+
+Run `nvidia-smi` inside the pod:
+
+```bash
+kubectl exec -it hami-quick-test -- nvidia-smi
+```
+
+Expected output (truncated):
+
+```text
+[HAMI-core Msg(...)]: Initializing.....
++-----------------------------------------------------------------------------------------+
+| NVIDIA-SMI ...                                                                          |
+|=========================================+========================+======================|
+|   0  <GPU model>                     On |   ...                  |                    0 |
+|                                         |           0MiB /  4096MiB |      0%      Default |
++-----------------------------------------+------------------------+----------------------+
+```
+
+The `Total Memory` column shows `4096MiB`, not the full physical GPU memory. This confirms HAMi is enforcing the virtual GPU memory limit.
+
+## Cleanup
+
+```bash
+kubectl delete pod hami-quick-test
+```
+
+## Next steps
+
+- [Online Installation](../installation/online-installation) - full Helm options, custom values
+- [Validate HAMi](./verify-hami) - deeper validation including native GPU stack checks
+- [Configure HAMi](../userguide/configure) - resource limits, scheduling policies, and more
+- [Device Sharing](../key-features/device-sharing) - how GPU sharing works under the hood

sidebars.js

@@ -37,6 +37,7 @@ module.exports = {
         "description": "Install and run HAMi quickly with a guided first deployment path."
       },
       "items": [
+        "get-started/quick-start",
         "get-started/deploy-with-helm",
         "get-started/verify-hami"
       ]