diff --git a/src/super_gradients/training/pipelines/pipelines.py b/src/super_gradients/training/pipelines/pipelines.py
index f099eb592d..ec79d9e5ed 100644
--- a/src/super_gradients/training/pipelines/pipelines.py
+++ b/src/super_gradients/training/pipelines/pipelines.py
@@ -24,7 +24,7 @@
     ImagesClassificationPrediction,
     ClassificationPrediction,
 )
-from super_gradients.training.utils.utils import generate_batch
+from super_gradients.training.utils.utils import generate_batch, infer_model_device, resolve_torch_device
 from super_gradients.training.utils.media.video import load_video, includes_video_extension
 from super_gradients.training.utils.media.image import ImageSource, check_image_typing
 from super_gradients.training.utils.media.stream import WebcamStreaming
@@ -68,9 +68,13 @@ def __init__(
         fuse_model: bool = True,
         dtype: Optional[torch.dtype] = None,
     ):
-        self.device = device or next(model.parameters()).device
-        self.model = model.to(self.device)
+        model_device: torch.device = infer_model_device(model=model)
+        if device:
+            device: torch.device = resolve_torch_device(device=device)
+
+        self.device: torch.device = device or model_device
         self.dtype = dtype or next(model.parameters()).dtype
+        self.model = model.to(device) if device and device != model_device else model
         self.class_names = class_names
 
         if isinstance(image_processor, list):
@@ -169,8 +173,12 @@ def _generate_prediction_result_single_batch(self, images: Iterable[np.ndarray])
         :param images:  Iterable of numpy arrays representing images.
         :return:        Iterable of Results object, each containing the results of the prediction and the image.
         """
+        # Make sure the model is on the correct device, as it might have been moved after init
+        model_device: torch.device = infer_model_device(model=self.model)
+        if self.device != model_device:
+            self.model = self.model.to(self.device)
+
         images = list(images)  # We need to load all the images into memory, and to reuse it afterwards.
-        self.model = self.model.to(self.device)  # Make sure the model is on the correct device, as it might have been moved after init
 
         # Preprocess
         preprocessed_images, processing_metadatas = [], []
diff --git a/src/super_gradients/training/utils/utils.py b/src/super_gradients/training/utils/utils.py
index 2307ff6511..3de562e43c 100755
--- a/src/super_gradients/training/utils/utils.py
+++ b/src/super_gradients/training/utils/utils.py
@@ -664,6 +664,24 @@ def infer_model_device(model: nn.Module) -> Optional[torch.device]:
             return None
 
 
+def resolve_torch_device(device: Union[str, torch.device]) -> torch.device:
+    """
+    Resolve the specified torch device. It accepts either a string or a torch.device object.
+
+    This function takes the provided device identifier and returns a corresponding torch.device object,
+    which represents the device where a torch.Tensor will be allocated.
+
+    :param device: A string or torch.device object representing the device (e.g., 'cpu', 'cuda', 'cuda:0').
+    :return: A torch.device object representing the resolved device.
+
+    Example:
+        >>> torch.cuda.set_device(5)
+        >>> str(resolve_torch_device("cuda"))
+        'cuda:5'
+    """
+    return torch.zeros([], device=device).device
+
+
 def check_model_contains_quantized_modules(model: nn.Module) -> bool:
     """
     Check if the model contains any quantized modules.