diff --git a/tests/framework/callbacks/test_torchsnapshot_saver.py b/tests/framework/callbacks/test_torchsnapshot_saver.py
index 5dfa14b93b..96354e253d 100644
--- a/tests/framework/callbacks/test_torchsnapshot_saver.py
+++ b/tests/framework/callbacks/test_torchsnapshot_saver.py
@@ -91,6 +91,32 @@ def test_save_every_n_train_epochs(self) -> None:
                 os.path.exists(expected_path) and os.path.isdir(expected_path)
             )
 
+    def test_save_on_train_end(self) -> None:
+        input_dim = 2
+        dataset_len = 10
+        batch_size = 2
+        max_epochs = 3
+        expected_steps_per_epoch = math.ceil(dataset_len / batch_size)
+        save_every_n_train_epochs = 2
+
+        my_unit = DummyTrainUnit(input_dim=input_dim)
+        dataloader = generate_random_dataloader(dataset_len, input_dim, batch_size)
+        state = init_train_state(dataloader=dataloader, max_epochs=max_epochs)
+        with tempfile.TemporaryDirectory() as temp_dir:
+            expected_path = os.path.join(
+                temp_dir,
+                f"epoch_{max_epochs}_step_{expected_steps_per_epoch * (max_epochs)}",
+            )
+            snapshot = TorchSnapshotSaver(
+                temp_dir,
+                save_every_n_epochs=save_every_n_train_epochs,
+                replicated=["**"],
+            )
+            train(state, my_unit, callbacks=[snapshot])
+            self.assertTrue(
+                os.path.exists(expected_path) and os.path.isdir(expected_path)
+            )
+
     def test_save_restore(self) -> None:
         input_dim = 2
         dataset_len = 10
diff --git a/torchtnt/framework/callbacks/torchsnapshot_saver.py b/torchtnt/framework/callbacks/torchsnapshot_saver.py
index 2c12e6ebf0..e7c7fe9e58 100644
--- a/torchtnt/framework/callbacks/torchsnapshot_saver.py
+++ b/torchtnt/framework/callbacks/torchsnapshot_saver.py
@@ -148,6 +148,16 @@ def on_train_epoch_end(self, state: State, unit: TTrainUnit) -> None:
         self._async_snapshot(snapshot_path, app_state, wait=True)
 
     def on_train_end(self, state: State, unit: TTrainUnit) -> None:
+        app_state = _get_app_state(state, unit, self._replicated, intra_epoch=False)
+
+        train_state = none_throws(state.train_state)
+        epoch = train_state.progress.num_epochs_completed
+        global_step = train_state.progress.num_steps_completed
+
+        # save snapshot to predetermined path
+        # TODO: discuss whether this path should be customized
+        snapshot_path = _get_snapshot_save_path(self._dirpath, epoch, global_step)
+        self._async_snapshot(snapshot_path, app_state, wait=False)
         self._wait()
 
     def on_exception(