diff --git a/deepspeed/runtime/pipe/module.py b/deepspeed/runtime/pipe/module.py
index 977ce599622f..e61dd1c72878 100644
--- a/deepspeed/runtime/pipe/module.py
+++ b/deepspeed/runtime/pipe/module.py
@@ -559,7 +559,18 @@ def save_state_dict(self, save_dir):
             model_ckpt_path = self.ckpt_layer_path(save_dir, idx)
             if not hasattr(layer, 'state_dict'):
                 continue
-            torch.save(layer.state_dict(), model_ckpt_path)
+            # We pass cloned tensors to torch.save() to avoid checkpoint bloat which occurs because torch.save()
+            # saves the underlying storage rather than the slice of the storage corresponding to individual tensors.
+            # This is a problem in DeepSpeed because we often allocate tensors using slices of large flattened buffers.
+            # Tensor cloning helps to avoid this problem because the storage of cloned tensors are closer to the true size.
+            # It is expected that the garbage collector will reclaim the cloned tensor storage to avoid memory bloat.
+            # See https://pytorch.org/docs/stable/notes/serialization.html#preserve-storage-sharing
+            orig_state_dict = layer.state_dict()
+            final_state_dict = type(orig_state_dict)(
+                {k: v.clone()
+                 for k,
+                 v in orig_state_dict.items()})
+            torch.save(final_state_dict, model_ckpt_path)
 
     def load_state_dir(self, load_dir, strict=True):
         for idx, layer in enumerate(self.forward_funcs):