diff --git a/src/lerobot/processor/observation_processor.py b/src/lerobot/processor/observation_processor.py
index 03f7d661d..12d1f82a2 100644
--- a/src/lerobot/processor/observation_processor.py
+++ b/src/lerobot/processor/observation_processor.py
@@ -52,8 +52,6 @@ class VanillaObservationProcessorStep(ObservationProcessorStep):
     -   Adds a batch dimension if one is not already present.
     """
 
-    device: str | None = None
-
     def _process_single_image(self, img: np.ndarray) -> Tensor:
         """
         Processes a single NumPy image array into a channel-first, normalized tensor.
@@ -73,9 +71,6 @@ class VanillaObservationProcessorStep(ObservationProcessorStep):
         # Convert to tensor
         img_tensor = torch.from_numpy(img)
 
-        if self.device is not None:
-            img_tensor = img_tensor.to(device=self.device)
-
         # Add batch dimension if needed
         if img_tensor.ndim == 3:
             img_tensor = img_tensor.unsqueeze(0)
diff --git a/tests/processor/test_observation_processor.py b/tests/processor/test_observation_processor.py
index e0208b30a..923059210 100644
--- a/tests/processor/test_observation_processor.py
+++ b/tests/processor/test_observation_processor.py
@@ -52,34 +52,6 @@ def test_process_single_image():
     assert processed_img.max() <= 1.0
 
 
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
-def test_process_single_image_cuda():
-    """Test processing a single image with CUDA device."""
-    processor = VanillaObservationProcessorStep(device="cuda")
-
-    # Create a mock image (H, W, C) format, uint8
-    image = np.random.randint(0, 256, size=(64, 64, 3), dtype=np.uint8)
-
-    observation = {"pixels": image}
-    transition = create_transition(observation=observation)
-
-    result = processor(transition)
-    processed_obs = result[TransitionKey.OBSERVATION]
-
-    # Check that the image was processed correctly
-    assert OBS_IMAGE in processed_obs
-    processed_img = processed_obs[OBS_IMAGE]
-
-    # Check shape: should be (1, 3, 64, 64) - batch, channels, height, width
-    assert processed_img.shape == (1, 3, 64, 64)
-
-    # Check dtype and range
-    assert processed_img.dtype == torch.float32
-    assert processed_img.device.type == "cuda"
-    assert processed_img.min() >= 0.0
-    assert processed_img.max() <= 1.0
-
-
 def test_process_image_dict():
     """Test processing multiple images in a dictionary."""
     processor = VanillaObservationProcessorStep()