diff --git a/deepmd/dpmodel/common.py b/deepmd/dpmodel/common.py
index 101badaab5..f5462ec4d0 100644
--- a/deepmd/dpmodel/common.py
+++ b/deepmd/dpmodel/common.py
@@ -24,7 +24,10 @@
     "int32": np.int32,
     "int64": np.int64,
     "default": GLOBAL_NP_FLOAT_PRECISION,
-    # NumPy doesn't have bfloat16 (and does't plan to add). Use float32 as a substitute.
+    # NumPy doesn't have bfloat16 (and does't plan to add)
+    # ml_dtypes is a solution, but it seems not supporting np.save/np.load
+    # hdf5 hasn't supported bfloat16 as well (see https://forum.hdfgroup.org/t/11975)
+    # Use float32 as a substitute.
     "bfloat16": np.float32,
 }
 assert VALID_PRECISION.issubset(PRECISION_DICT.keys())
diff --git a/deepmd/pt/utils/utils.py b/deepmd/pt/utils/utils.py
index 3337036ca9..68efc3cc66 100644
--- a/deepmd/pt/utils/utils.py
+++ b/deepmd/pt/utils/utils.py
@@ -10,6 +10,7 @@
 import torch.nn.functional as F
 
 from deepmd.dpmodel.common import PRECISION_DICT as NP_PRECISION_DICT
+from deepmd.dpmodel.common import RESERVED_PRECISON_DICT as NP_RESERVED_PRECISON_DICT
 
 from .env import (
     DEVICE,
@@ -103,7 +104,9 @@ def to_torch_tensor(
         return None
     assert xx is not None
     # Create a reverse mapping of NP_PRECISION_DICT
-    reverse_precision_dict = {v: k for k, v in NP_PRECISION_DICT.items()}
+    # unsafe considering bfloat16:
+    # reverse_precision_dict = {v: k for k, v in NP_PRECISION_DICT.items()}
+    reverse_precision_dict = NP_RESERVED_PRECISON_DICT
     # Use the reverse mapping to find keys with the desired value
     prec = reverse_precision_dict.get(xx.dtype.type, None)
     prec = PT_PRECISION_DICT.get(prec, None)