diff --git a/tests/test_thunder_fsdp.py b/tests/test_thunder_fsdp.py
index 321cdac7a6..94d874520d 100644
--- a/tests/test_thunder_fsdp.py
+++ b/tests/test_thunder_fsdp.py
@@ -263,8 +263,6 @@ def set_up_planner(self, state_dict, metadata, is_coordinator):
 
 @RunIf(min_cuda_gpus=2, thunder=True, standalone=True)
 def test_save_load_sharded_checkpoint(tmp_path):
-    pytest.skip("Temporarily disabled, often exceeds 5 min timeout")
-
     strategy = ThunderFSDPStrategy(state_dict_type="sharded", broadcast_from=0)
     fabric = Fabric(accelerator="cuda", devices=2, strategy=strategy)
     fabric.launch()
@@ -274,8 +272,10 @@ def test_save_load_sharded_checkpoint(tmp_path):
 
     # save a sharded model
     model = fabric.setup(model)
+    print("Past fabric.setup()")
     state = {"model": model, "stateful": StatefulThing(), "primitive": 123}
     fabric.save(tmp_path, state)
+    print("Past fabric.save()")
 
     # assert the file contents
     if fabric.global_rank == 0:
@@ -295,17 +295,21 @@ def test_save_load_sharded_checkpoint(tmp_path):
             }
         }
         torch.testing.assert_close(checkpoint["model"], expected)
+    print("Past rank0 save checks()")
 
     # load its weights into a different sharded model
     model = MyModel(4)
     model = fabric.setup(model)
+    print("Past fabric.setup() 2")
     state = {"model": model, "stateful": StatefulThing(), "primitive": 321}
     fabric.load(tmp_path, state)
+    print("Past fabric.load()")
 
     from thunder.distributed import _unshard_params
 
     # unshard this model's parameters to compare with the original state dict before sharding
     _unshard_params(model, model.process_group_for_ddp, True)
+    print("Past unshard_params")
     # we loaded rank 0's weights, so this would fail in the other ranks
     if fabric.global_rank == 0:
         actual = model.state_dict()
@@ -313,6 +317,7 @@ def test_save_load_sharded_checkpoint(tmp_path):
         assert actual["buf"].device.type == "cuda"
         actual["buf"] = actual["buf"].to(device="cpu")
         torch.testing.assert_close(actual, expected)
+    print("Past rank0 shard checks()")
     assert state["primitive"] == 123