diff --git a/kronfluence/computer/factor_computer.py b/kronfluence/computer/factor_computer.py
index fbe9657..db8a76d 100644
--- a/kronfluence/computer/factor_computer.py
+++ b/kronfluence/computer/factor_computer.py
@@ -299,6 +299,19 @@ def fit_covariance_matrices(
                     )
 
                 self._reset_memory()
+
+
+                import gc
+                for obj in gc.get_objects():
+                    try:
+                        if torch.is_tensor(obj) or (
+                                hasattr(obj, 'data') and torch.is_tensor(obj.data)) and obj.device == torch.device(
+                                "cuda"):
+                            print(type(obj), obj.size())
+                    except:
+                        pass
+
+
                 start_time = get_time(state=self.state)
                 with self.profiler.profile("Fit Covariance"):
                     loader = self._get_dataloader(
@@ -323,6 +336,18 @@ def fit_covariance_matrices(
                     f"Fitted covariance matrices with {num_data_processed.item()} data points in "
                     f"{elapsed_time:.2f} seconds."
                 )
+                self._reset_memory()
+
+                print("Done")
+                import gc
+                for obj in gc.get_objects():
+                    try:
+                        if torch.is_tensor(obj) or (
+                                hasattr(obj, 'data') and torch.is_tensor(obj.data)) and obj.device == torch.device(
+                                "cuda"):
+                            print(type(obj), obj.size())
+                    except:
+                        pass
 
                 with self.profiler.profile("Save Covariance"):
                     if self.state.is_main_process:
diff --git a/kronfluence/factor/covariance.py b/kronfluence/factor/covariance.py
index 00da00c..e71b550 100644
--- a/kronfluence/factor/covariance.py
+++ b/kronfluence/factor/covariance.py
@@ -183,14 +183,6 @@ def fit_covariance_matrices_with_loader(
             - Number of data points processed.
             - Computed covariance matrices (nested dict: factor_name -> module_name -> tensor).
     """
-    import gc
-    for obj in gc.get_objects():
-        try:
-            if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)) and obj.device == torch.device("cuda"):
-                print(type(obj), obj.size())
-        except:
-            pass
-
     update_factor_args(model=model, factor_args=factor_args)
     if tracked_module_names is None:
         tracked_module_names = get_tracked_module_names(model=model)
@@ -243,13 +235,6 @@ def fit_covariance_matrices_with_loader(
             total_steps += 1
             pbar.update(1)
 
-    for obj in gc.get_objects():
-        try:
-            if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)) and obj.device == torch.device("cuda"):
-                print(type(obj), obj.size())
-        except:
-            pass
-
     if state.use_distributed:
         synchronize_modules(model=model, tracked_module_names=tracked_module_names)
         num_data_processed = num_data_processed.to(device=state.device)
@@ -276,11 +261,4 @@ def fit_covariance_matrices_with_loader(
     set_mode(model=model, mode=ModuleMode.DEFAULT, release_memory=True)
     state.wait_for_everyone()
 
-    for obj in gc.get_objects():
-        try:
-            if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)) and obj.device == torch.device("cuda"):
-                print(type(obj), obj.size())
-        except:
-            pass
-
     return num_data_processed, saved_factors