diff --git a/src/transformers/utils/import_utils.py b/src/transformers/utils/import_utils.py
index 21f8ef0260fe1c..69cb1f05163def 100644
--- a/src/transformers/utils/import_utils.py
+++ b/src/transformers/utils/import_utils.py
@@ -312,31 +312,7 @@ def is_torch_bf16_gpu_available():
 
     import torch
 
-    # since currently no utility function is available we build our own.
-    # some bits come from https://github.com/pytorch/pytorch/blob/2289a12f21c54da93bf5d696e3f9aea83dd9c10d/torch/testing/_internal/common_cuda.py#L51
-    # with additional check for torch version
-    # to succeed:
-    # 1. torch >= 1.10 (1.9 should be enough for AMP API has changed in 1.10, so using 1.10 as minimal)
-    # 2. the hardware needs to support bf16 (GPU arch >= Ampere, or CPU)
-    # 3. if using gpu, CUDA >= 11
-    # 4. torch.autocast exists
-    # XXX: one problem here is that it may give invalid results on mixed gpus setup, so it's
-    # really only correct for the 0th gpu (or currently set default device if different from 0)
-    if version.parse(version.parse(torch.__version__).base_version) < version.parse("1.10"):
-        return False
-
-    if torch.cuda.is_available() and torch.version.cuda is not None:
-        if torch.cuda.get_device_properties(torch.cuda.current_device()).major < 8:
-            return False
-        if int(torch.version.cuda.split(".")[0]) < 11:
-            return False
-        if not hasattr(torch.cuda.amp, "autocast"):
-            return False
-    else:
-        return False
-
-    return True
-
+    return torch.cuda.is_bf16_supported()
 
 def is_torch_bf16_cpu_available():
     if not is_torch_available():