diff --git a/pytorch_lightning/plugins/training_type/ddp_spawn.py b/pytorch_lightning/plugins/training_type/ddp_spawn.py
index b958d4808e1c2..0db0dd8784a06 100644
--- a/pytorch_lightning/plugins/training_type/ddp_spawn.py
+++ b/pytorch_lightning/plugins/training_type/ddp_spawn.py
@@ -34,8 +34,6 @@
 from pytorch_lightning.trainer.states import TrainerFn
 from pytorch_lightning.utilities import _TORCH_GREATER_EQUAL_1_8, rank_zero_warn
 from pytorch_lightning.utilities.apply_func import apply_to_collection, move_data_to_device
-from pytorch_lightning.utilities.cloud_io import atomic_save
-from pytorch_lightning.utilities.cloud_io import load as pl_load
 from pytorch_lightning.utilities.distributed import distributed_available
 from pytorch_lightning.utilities.distributed import group as _group
 from pytorch_lightning.utilities.distributed import (
@@ -286,7 +284,7 @@ def __transfer_distrib_spawn_state_on_fit_end(self, trainer: "pl.Trainer", resul
             last_path = None
             if trainer.state.fn == TrainerFn.FITTING and best_model_path is not None and len(best_model_path) > 0:
                 last_path = re.sub(".ckpt", ".tmp_end.ckpt", best_model_path)
-                atomic_save(state_dict, last_path)
+                self.checkpoint_io.save_checkpoint(state_dict, last_path)
 
             # todo, pass complete checkpoint as state dictionary
             self.mp_queue.put(best_model_path)
@@ -307,7 +305,7 @@ def __recover_child_process_weights(self, best_path, last_path):
 
         # load last weights
         if last_path is not None and self.lightning_module.trainer.state.fn == TrainerFn.FITTING:
-            ckpt = pl_load(last_path, map_location=lambda storage, loc: storage)
+            ckpt = self.checkpoint_io.load_checkpoint(last_path, map_location=(lambda storage, loc: storage))
             self.lightning_module.load_state_dict(ckpt)
 
     def barrier(self, *args, **kwargs) -> None:
diff --git a/pytorch_lightning/plugins/training_type/tpu_spawn.py b/pytorch_lightning/plugins/training_type/tpu_spawn.py
index a7258fc7123c1..1afb8e6c6d767 100644
--- a/pytorch_lightning/plugins/training_type/tpu_spawn.py
+++ b/pytorch_lightning/plugins/training_type/tpu_spawn.py
@@ -222,7 +222,7 @@ def __transfer_distrib_spawn_state_on_fit_end(self, trainer: "pl.Trainer", resul
             last_path = None
             if trainer.state.fn == TrainerFn.FITTING and best_model_path is not None and len(best_model_path) > 0:
                 last_path = re.sub(".ckpt", ".tmp_end.ckpt", best_model_path)
-                self.save(state_dict, last_path)
+                self.checkpoint_io.save_checkpoint(state_dict, last_path)
 
             if self.local_rank == 0:
                 # todo, pass complete checkpoint as state dictionary
@@ -231,9 +231,6 @@ def __transfer_distrib_spawn_state_on_fit_end(self, trainer: "pl.Trainer", resul
                 self.mp_queue.put(results)
                 self.lightning_module.add_to_queue(self.mp_queue)  # adds the `callback_metrics` to the queue
 
-    def save(self, state_dict: Dict, path: _PATH) -> None:
-        xm.save(state_dict, path)
-
     def broadcast(self, obj: object, src: int = 0) -> object:
         if not self.is_distributed:
             return obj