diff --git a/test/test_input_output_aliases.py b/test/test_input_output_aliases.py
index 8e7b0a7ed6e..cae60b7889d 100644
--- a/test/test_input_output_aliases.py
+++ b/test/test_input_output_aliases.py
@@ -1,3 +1,4 @@
+import os
 import sys
 
 import torch
@@ -162,6 +163,28 @@ def test_separate_graphs(self):
 
     self.assertEqual(t1.item(), 3)
 
+  def test_xm_save_no_aliasing(self):
+    """
+    Test that xm.save() does not perform aliasing.
+    """
+    xla_device = xm.xla_device()
+    t0 = torch.tensor([1], device=xla_device)
+    t1 = torch.tensor([2], device=xla_device)
+    xm.mark_step()
+
+    t2 = t0 + t1
+    t1.add_(1)
+
+    # Save the new value of t1 should not result in the old value
+    # being donated...
+    xm.save(t1, os.devnull)
+
+    # otherwise this mark_step could crash, or compute the wrong value
+    # for t2.
+    xm.mark_step()
+
+    self.assertEqual(t2.item(), 3)
+
 
 if __name__ == '__main__':
   test = unittest.main()
diff --git a/torch_xla/core/xla_model.py b/torch_xla/core/xla_model.py
index 6e1936c258a..4f3da8d650a 100644
--- a/torch_xla/core/xla_model.py
+++ b/torch_xla/core/xla_model.py
@@ -1278,7 +1278,7 @@ def _maybe_convert_to_cpu(data: Any, convert: bool = True) -> ToXlaTensorArena:
 
   def convert_fn(tensors):
     torch_xla._XLAC._xla_sync_multi(
-        tensors, devices=[], wait=True, sync_xla_data=True)
+        tensors, devices=[], wait=True, sync_xla_data=False)
     if not convert:
       return tensors
     return torch_xla._XLAC._xla_get_cpu_tensors(tensors)
diff --git a/torch_xla/utils/serialization.py b/torch_xla/utils/serialization.py
index ed3797ad945..05cfa93e2ea 100644
--- a/torch_xla/utils/serialization.py
+++ b/torch_xla/utils/serialization.py
@@ -25,7 +25,7 @@ def _rewrite_data(path, data, save_tensors):
 
   def convert_fn(tensors):
     torch_xla._XLAC._xla_sync_multi(
-        tensors, devices=[], wait=True, sync_xla_data=True)
+        tensors, devices=[], wait=True, sync_xla_data=False)
     rewritten_tensors = []
     for i, t in enumerate(tensors):
       if save_tensors: