diff --git a/src/lightning_lite/lite.py b/src/lightning_lite/lite.py
index a198efd1ceab8..5cd33f7c1baea 100644
--- a/src/lightning_lite/lite.py
+++ b/src/lightning_lite/lite.py
@@ -218,8 +218,9 @@ def setup_module(self, module: nn.Module, move_to_device: bool = True) -> _LiteM
         module = self._strategy.setup_module(module)
         module = _LiteModule(module, self._precision, original_module=original_module)
 
-        # Update the _DeviceDtypeModuleMixin's device parameter
-        module.to(self.device if move_to_device else next(module.parameters()).device)
+        if not isinstance(self._strategy, FSDPStrategy):
+            # Update the _DeviceDtypeModuleMixin's device parameter
+            module.to(self.device if move_to_device else next(module.parameters()).device)
 
         self._models_setup += 1
         return module
diff --git a/tests/tests_lite/strategies/test_fsdp_integration.py b/tests/tests_lite/strategies/test_fsdp_integration.py
index 052133e265e4c..963918d076d77 100644
--- a/tests/tests_lite/strategies/test_fsdp_integration.py
+++ b/tests/tests_lite/strategies/test_fsdp_integration.py
@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import tempfile
+from unittest import mock
 
 import pytest
 import torch
@@ -116,3 +117,29 @@ def test_fsdp_train_save_load(manual_wrapping, precision):
         lite._strategy.save_checkpoint(model.state_dict(), ckpt_path)
 
     _assert_save_equality(lite, model, ckpt_path)
+
+
+@RunIf(min_cuda_gpus=2, skip_windows=True, standalone=True, min_torch="1.12")
+@pytest.mark.parametrize("move_to_device", [True, False])
+@mock.patch("lightning_lite.wrappers._LiteModule")
+def test_setup_module_move_to_device(lite_module_mock, move_to_device):
+    """Test that `move_to_device` does nothing, FSDP decides which device parameters get moved to which device
+    (sharding)."""
+    strategy = FSDPStrategy(auto_wrap_policy=_custom_auto_wrap_policy)
+    lite = LightningLite(accelerator="cuda", devices=2, strategy=strategy)
+    lite.launch()
+
+    model = torch.nn.Linear(10, 10, bias=False)  # total params: 10 * 10 = 100
+    lite_model = lite.setup_module(model, move_to_device=move_to_device)
+    lite_module_mock.assert_not_called()
+
+    assert list(param.device for param in model.parameters()) == []
+    assert len(list(lite_model.parameters())) == 1
+
+    # the linear layer got sharded and each part is on the expected device
+    assert next(lite_model.parameters()).device == torch.device("cuda", lite.local_rank)
+    assert next(lite_model.parameters()).numel() == 50
+
+    # The _DeviceDtypeModuleMixin currently can't represent the device in a meaningful way for sharded models
+    assert lite_model.device == torch.device("cpu")
+    assert lite.device == torch.device("cuda", lite.local_rank)