diff --git a/bindings/pyroot/pythonizations/python/ROOT/_pythonization/_ml_dataloader.py b/bindings/pyroot/pythonizations/python/ROOT/_pythonization/_ml_dataloader.py
index d2ea35a73c4c7..1d50a57335f5c 100644
--- a/bindings/pyroot/pythonizations/python/ROOT/_pythonization/_ml_dataloader.py
+++ b/bindings/pyroot/pythonizations/python/ROOT/_pythonization/_ml_dataloader.py
@@ -387,7 +387,7 @@ def ConvertBatchToNumpy(self, batch) -> np.ndarray:
 
         return return_data
 
-    def ConvertBatchToPyTorch(self, batch: Any) -> torch.Tensor:
+    def ConvertBatchToPyTorch(self, batch: Any, device=None) -> torch.Tensor:
         """Convert a RTensor into a PyTorch tensor
 
         Args:
@@ -404,7 +404,7 @@ def ConvertBatchToPyTorch(self, batch: Any) -> torch.Tensor:
 
         data.reshape((batch_size * num_columns,))
 
-        return_data = torch.as_tensor(np.asarray(data)).reshape(batch_size, num_columns)
+        return_data = torch.as_tensor(np.asarray(data), device=device).reshape(batch_size, num_columns)
 
         # Splice target column from the data if target is given
         if self.target_given:
@@ -704,12 +704,16 @@ def as_numpy(self) -> FormattedLoader:
         self._ensure_created()
         return FormattedLoader(self._internal, self._internal.ConvertBatchToNumpy, self._is_training)
 
-    def as_torch(self) -> FormattedLoader:
+    def as_torch(self, device: str | torch.device | None = None) -> FormattedLoader:
         """
         Return an iterable that yields batches as PyTorch tensors.
+
+        Args:
+            device: If given, the returned tensors are moved to the specified device.
         """
         self._ensure_created()
-        return FormattedLoader(self._internal, self._internal.ConvertBatchToPyTorch, self._is_training)
+        conversion_fn = lambda batch: self._internal.ConvertBatchToPyTorch(batch, device)  # noqa: E731
+        return FormattedLoader(self._internal, conversion_fn, self._is_training)
 
     def as_tensorflow(self) -> tf.data.Dataset:
         """
diff --git a/bindings/pyroot/pythonizations/test/ml_dataloader.py b/bindings/pyroot/pythonizations/test/ml_dataloader.py
index 647cf6cd605f1..2d49c2a11dc13 100644
--- a/bindings/pyroot/pythonizations/test/ml_dataloader.py
+++ b/bindings/pyroot/pythonizations/test/ml_dataloader.py
@@ -695,7 +695,7 @@ def test12_PyTorch(self):
             collected_z_train = []
             collected_z_val = []
 
-            iter_train = iter(gen_train.as_torch())
+            iter_train = iter(gen_train.as_torch(device="cpu"))
             iter_val = iter(gen_validation.as_torch())
 
             for _ in range(self.n_train_batch):
@@ -1792,7 +1792,7 @@ def test12_PyTorch(self):
             collected_z_val = []
 
             iter_train = iter(gen_train.as_torch())
-            iter_val = iter(gen_validation.as_torch())
+            iter_val = iter(gen_validation.as_torch(device="cpu"))
 
             for _ in range(self.n_train_batch):
                 x, y, z = next(iter_train)
@@ -2984,7 +2984,7 @@ def test12_PyTorch(self):
             collected_z_train = []
             collected_z_val = []
 
-            iter_train = iter(gen_train.as_torch())
+            iter_train = iter(gen_train.as_torch(device="cpu"))
             iter_val = iter(gen_validation.as_torch())
 
             for _ in range(self.n_train_batch):
@@ -4133,7 +4133,7 @@ def test12_PyTorch(self):
             collected_z_val = []
 
             iter_train = iter(gen_train.as_torch())
-            iter_val = iter(gen_validation.as_torch())
+            iter_val = iter(gen_validation.as_torch(device="cpu"))
 
             for _ in range(self.n_train_batch):
                 x, y, z = next(iter_train)
@@ -5546,7 +5546,7 @@ def test12_PyTorch(self):
             collected_z_train = []
             collected_z_val = []
 
-            iter_train = iter(gen_train.as_torch())
+            iter_train = iter(gen_train.as_torch(device="cpu"))
             iter_val = iter(gen_validation.as_torch())
 
             for _ in range(self.n_train_batch):
@@ -7086,7 +7086,7 @@ def test12_PyTorch(self):
             collected_z_val = []
 
             iter_train = iter(gen_train.as_torch())
-            iter_val = iter(gen_validation.as_torch())
+            iter_val = iter(gen_validation.as_torch(device="cpu"))
 
             for _ in range(self.n_train_batch):
                 x, y, z = next(iter_train)