diff --git a/paddlenlp/trainer/unified_checkpoint/async_handler.py b/paddlenlp/trainer/unified_checkpoint/async_handler.py
index ffe098808c2f..7e8e11a0e892 100644
--- a/paddlenlp/trainer/unified_checkpoint/async_handler.py
+++ b/paddlenlp/trainer/unified_checkpoint/async_handler.py
@@ -80,7 +80,8 @@ def _file_save_async_or_sync(
                 if isinstance(state_dict[k], paddle.Tensor):
                     state_dict[k] = state_dict.pop(k).cpu().numpy()
 
-            state_dict = quant_unified_optimizer(state_dict, state_dict_type, ckpt_quant_stage)
+            if state_dict_type == "optimizer_weight" and ckpt_quant_stage != "O0":
+                state_dict = quant_unified_optimizer(state_dict, state_dict_type, ckpt_quant_stage)
             safe_save_file(state_dict, path, metadata={"format": "np"})
         else:
             if len(state_dict.keys()) == 0:
@@ -206,9 +207,10 @@ def _save_file_async_in_process(
                 signal_path = shared_save_signal_path[:].decode("utf-8").rstrip("\x00")
                 logger.info(f"Start to async save {path}")
                 state_dict = _read_state_dict_from_shm(meta_dict, shm)  # numpy array
-                state_dict = quant_unified_optimizer(
-                    state_dict, state_dict_type, ckpt_quant_stage, async_save=True
-                )  # ckpt quantization
+                if state_dict_type == "optimizer_weight" and ckpt_quant_stage != "O0":
+                    state_dict = quant_unified_optimizer(
+                        state_dict, state_dict_type, ckpt_quant_stage, async_save=True
+                    )  # ckpt quantization
                 safe_save_file(state_dict, path, {"format": "np"})
                 del state_dict
                 saved_signal_path = os.path.join(signal_path, f".{state_dict_type}.done.{global_rank}")