diff --git a/pytorch_lightning/plugins/training_type/ddp_spawn.py b/pytorch_lightning/plugins/training_type/ddp_spawn.py
index 0db0dd8784a06..3bfe89be22467 100644
--- a/pytorch_lightning/plugins/training_type/ddp_spawn.py
+++ b/pytorch_lightning/plugins/training_type/ddp_spawn.py
@@ -277,7 +277,7 @@ def __transfer_distrib_spawn_state_on_fit_end(self, trainer: "pl.Trainer", resul
         # requires to compute the state_dict on all processes in case Metrics are present
         state_dict = self.lightning_module.state_dict()
 
-        if self.global_rank == 0 and self.mp_queue is not None:
+        if self.should_rank_save_checkpoint and self.mp_queue is not None:
             rank_zero_warn("cleaning up ddp environment...")
 
             # save the last weights
diff --git a/pytorch_lightning/plugins/training_type/tpu_spawn.py b/pytorch_lightning/plugins/training_type/tpu_spawn.py
index 1afb8e6c6d767..7f81bd15e012b 100644
--- a/pytorch_lightning/plugins/training_type/tpu_spawn.py
+++ b/pytorch_lightning/plugins/training_type/tpu_spawn.py
@@ -215,21 +215,21 @@ def __transfer_distrib_spawn_state_on_fit_end(self, trainer: "pl.Trainer", resul
         # requires to compute the state_dict on all processes in case Metrics are present
         state_dict = self.lightning_module.state_dict()
 
-        if self.mp_queue is not None:
-            rank_zero_warn("cleaning up tpu spawn environment...")
+        assert self.mp_queue is not None
+        rank_zero_warn("cleaning up tpu spawn environment...")
 
+        if self.local_rank == 0:
             # save the last weights
             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.checkpoint_io.save_checkpoint(state_dict, last_path)
 
-            if self.local_rank == 0:
-                # todo, pass complete checkpoint as state dictionary
-                self.mp_queue.put(best_model_path)
-                self.mp_queue.put(last_path)
-                self.mp_queue.put(results)
-                self.lightning_module.add_to_queue(self.mp_queue)  # adds the `callback_metrics` to the queue
+            # todo, pass complete checkpoint as state dictionary
+            self.mp_queue.put(best_model_path)
+            self.mp_queue.put(last_path)
+            self.mp_queue.put(results)
+            self.lightning_module.add_to_queue(self.mp_queue)  # adds the `callback_metrics` to the queue
 
     def broadcast(self, obj: object, src: int = 0) -> object:
         if not self.is_distributed: