diff --git a/examples/quickstart-pytorch/README.md b/examples/quickstart-pytorch/README.md
index 02c9b4b3849..978191cc0ec 100644
--- a/examples/quickstart-pytorch/README.md
+++ b/examples/quickstart-pytorch/README.md
@@ -47,6 +47,8 @@ Write the command below in your terminal to install the dependencies according t
 pip install -r requirements.txt
 ```
 
+______________________________________________________________________
+
 ## Run Federated Learning with PyTorch and Flower
 
 Afterwards you are ready to start the Flower server as well as the clients. You can simply start the server in a terminal as follows:
@@ -72,3 +74,29 @@ python3 client.py --partition-id 1
 ```
 
 You will see that PyTorch is starting a federated training. Look at the [code](https://github.com/adap/flower/tree/main/examples/quickstart-pytorch) for a detailed explanation.
+
+______________________________________________________________________
+
+## Run Federated Learning with PyTorch and `Flower Next`
+
+### 1. Start the long-running Flower server (SuperLink)
+
+```bash
+flower-superlink --insecure
+```
+
+### 2. Start the long-running Flower clients (SuperNodes)
+
+Start 2 Flower `SuperNodes` in 2 separate terminal windows, using:
+
+```bash
+flower-client-app client:app --insecure
+```
+
+### 3. Run the Flower App
+
+With both the long-running server (SuperLink) and two clients (SuperNode) up and running, we can now run the actual Flower App:
+
+```bash
+flower-server-app server:app --insecure
+```
diff --git a/examples/quickstart-pytorch/client.py b/examples/quickstart-pytorch/client.py
index e640ce111df..e58dbf7ea0b 100644
--- a/examples/quickstart-pytorch/client.py
+++ b/examples/quickstart-pytorch/client.py
@@ -2,7 +2,7 @@
 import warnings
 from collections import OrderedDict
 
-import flwr as fl
+from flwr.client import NumPyClient, ClientApp
 from flwr_datasets import FederatedDataset
 import torch
 import torch.nn as nn
@@ -99,11 +99,11 @@ def apply_transforms(batch):
 parser.add_argument(
     "--partition-id",
     choices=[0, 1, 2],
-    required=True,
+    default=0,
     type=int,
     help="Partition of the dataset divided into 3 iid partitions created artificially.",
 )
-partition_id = parser.parse_args().partition_id
+partition_id = parser.parse_known_args()[0].partition_id
 
 # Load model and data (simple CNN, CIFAR-10)
 net = Net().to(DEVICE)
@@ -111,7 +111,7 @@ def apply_transforms(batch):
 
 
 # Define Flower client
-class FlowerClient(fl.client.NumPyClient):
+class FlowerClient(NumPyClient):
     def get_parameters(self, config):
         return [val.cpu().numpy() for _, val in net.state_dict().items()]
 
@@ -131,8 +131,22 @@ def evaluate(self, parameters, config):
         return loss, len(testloader.dataset), {"accuracy": accuracy}
 
 
-# Start Flower client
-fl.client.start_client(
-    server_address="127.0.0.1:8080",
-    client=FlowerClient().to_client(),
+def client_fn(cid: str):
+    """Create and return an instance of Flower `Client`."""
+    return FlowerClient().to_client()
+
+
+# Flower ClientApp
+app = ClientApp(
+    client_fn=client_fn,
 )
+
+
+# Legacy mode
+if __name__ == "__main__":
+    from flwr.client import start_client
+
+    start_client(
+        server_address="127.0.0.1:8080",
+        client=FlowerClient().to_client(),
+    )
diff --git a/examples/quickstart-pytorch/pyproject.toml b/examples/quickstart-pytorch/pyproject.toml
index d8e1503dd8a..7255e627471 100644
--- a/examples/quickstart-pytorch/pyproject.toml
+++ b/examples/quickstart-pytorch/pyproject.toml
@@ -10,7 +10,7 @@ authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
-flwr = ">=1.0,<2.0"
+flwr = ">=1.8.0,<2.0"
 flwr-datasets = { extras = ["vision"], version = ">=0.0.2,<1.0.0" }
 torch = "2.1.1"
 torchvision = "0.16.1"
diff --git a/examples/quickstart-pytorch/requirements.txt b/examples/quickstart-pytorch/requirements.txt
index 4e321e2cd0c..12627809551 100644
--- a/examples/quickstart-pytorch/requirements.txt
+++ b/examples/quickstart-pytorch/requirements.txt
@@ -1,4 +1,4 @@
-flwr>=1.0, <2.0
+flwr>=1.8.0, <2.0
 flwr-datasets[vision]>=0.0.2, <1.0.0
 torch==2.1.1
 torchvision==0.16.1
diff --git a/examples/quickstart-pytorch/server.py b/examples/quickstart-pytorch/server.py
index fe691a88aba..4034703ca69 100644
--- a/examples/quickstart-pytorch/server.py
+++ b/examples/quickstart-pytorch/server.py
@@ -1,6 +1,7 @@
 from typing import List, Tuple
 
-import flwr as fl
+from flwr.server import ServerApp, ServerConfig
+from flwr.server.strategy import FedAvg
 from flwr.common import Metrics
 
 
@@ -15,11 +16,26 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
 
 
 # Define strategy
-strategy = fl.server.strategy.FedAvg(evaluate_metrics_aggregation_fn=weighted_average)
+strategy = FedAvg(evaluate_metrics_aggregation_fn=weighted_average)
 
-# Start Flower server
-fl.server.start_server(
-    server_address="0.0.0.0:8080",
-    config=fl.server.ServerConfig(num_rounds=3),
+
+# Define config
+config = ServerConfig(num_rounds=3)
+
+
+# Flower ServerApp
+app = ServerApp(
+    config=config,
     strategy=strategy,
 )
+
+
+# Legacy mode
+if __name__ == "__main__":
+    from flwr.server import start_server
+
+    start_server(
+        server_address="0.0.0.0:8080",
+        config=config,
+        strategy=strategy,
+    )