diff --git a/examples/app_server_with_load_balancer/app.py b/examples/app_server_with_load_balancer/app.py
new file mode 100644
index 00000000000000..a5e24eac335fbf
--- /dev/null
+++ b/examples/app_server_with_load_balancer/app.py
@@ -0,0 +1,59 @@
+# !pip install torchvision pydantic
+import base64
+import io
+
+import torch
+import torchvision
+from PIL import Image
+from pydantic import BaseModel
+
+import lightning as L
+from lightning.app.components.serve import Image as InputImage
+from lightning.app.components.serve import PythonServer
+
+
+class PyTorchServer(PythonServer):
+    def __init__(self):
+        super().__init__(
+            input_type=InputImage,
+            output_type=OutputData,
+            cloud_compute=L.CloudCompute("gpu"),
+        )
+
+    def setup(self):
+        self._model = torchvision.models.resnet18(pretrained=True)
+        self._device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        self._model.to(self._device)
+
+    def predict(self, request):
+        image = base64.b64decode(request.image.encode("utf-8"))
+        image = Image.open(io.BytesIO(image))
+        transforms = torchvision.transforms.Compose(
+            [
+                torchvision.transforms.Resize(224),
+                torchvision.transforms.ToTensor(),
+                torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+            ]
+        )
+        image = transforms(image)
+        image = image.to(self._device)
+        prediction = self._model(image.unsqueeze(0))
+        return {"prediction": prediction.argmax().item()}
+
+
+class OutputData(BaseModel):
+    prediction: int
+
+
+app = L.LightningApp(PyTorchServer())
+
+
+# TODO: name confusion LoadBalancer vs. AutoScaler
+# from lightning.app.components import LoadBalancer
+# component = LoadBalancer(
+#     PyTorchServer,
+#     num_replicas=4,
+#     balance_function="predict",
+#     auto_scale=False,
+# )
+# app = L.LightningApp(component)