diff --git a/examples/app/server_with_auto_scaler/app.py b/examples/app/server_with_auto_scaler/app.py
index 8e0907b8f2..8761119634 100644
--- a/examples/app/server_with_auto_scaler/app.py
+++ b/examples/app/server_with_auto_scaler/app.py
@@ -26,7 +26,12 @@ class PyTorchServer(L.app.components.PythonServer):
         )
 
     def setup(self):
-        self._device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        if torch.cuda.is_available():
+            self._device = torch.device("cuda:0")
+        elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
+            self._device = torch.device("mps")
+        else:
+            self._device = torch.device("cpu")
         self._model = torchvision.models.resnet18(pretrained=True).to(self._device)
 
     def predict(self, requests: BatchRequestModel):
diff --git a/examples/fabric/dcgan/train_torch.py b/examples/fabric/dcgan/train_torch.py
index 6362736107..d7ce548b16 100644
--- a/examples/fabric/dcgan/train_torch.py
+++ b/examples/fabric/dcgan/train_torch.py
@@ -69,7 +69,12 @@ def main():
     dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=workers)
 
     # Decide which device we want to run on
-    device = torch.device("cuda:0" if (torch.cuda.is_available() and num_gpus > 0) else "cpu")
+    if torch.cuda.is_available() and num_gpus > 0:
+        device = torch.device("cuda:0")
+    elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
+        device = torch.device("mps")
+    else:
+        device = torch.device("cpu")
 
     output_dir = Path("outputs-torch", time.strftime("%Y%m%d-%H%M%S"))
     output_dir.mkdir(parents=True, exist_ok=True)
diff --git a/examples/fabric/image_classifier/train_torch.py b/examples/fabric/image_classifier/train_torch.py
index e2bfd750f7..43f3128aba 100644
--- a/examples/fabric/image_classifier/train_torch.py
+++ b/examples/fabric/image_classifier/train_torch.py
@@ -56,7 +56,13 @@ def run(hparams):
     torch.manual_seed(hparams.seed)
 
     use_cuda = torch.cuda.is_available()
-    device = torch.device("cuda" if use_cuda else "cpu")
+    use_mps = torch.backends.mps.is_available()
+    if use_cuda:
+        device = torch.device("cuda")
+    elif use_mps:
+        device = torch.device("mps")
+    else:
+        device = torch.device("cpu")
 
     transform = T.Compose([T.ToTensor(), T.Normalize((0.1307,), (0.3081,))])
     train_dataset = MNIST(DATASETS_PATH, train=True, download=True, transform=transform)