diff --git a/freqtrade/freqai/base_models/BasePyTorchClassifier.py b/freqtrade/freqai/base_models/BasePyTorchClassifier.py
index 977152cc5..3a4de4df0 100644
--- a/freqtrade/freqai/base_models/BasePyTorchClassifier.py
+++ b/freqtrade/freqai/base_models/BasePyTorchClassifier.py
@@ -45,6 +45,7 @@ class BasePyTorchClassifier(BasePyTorchModel):
     ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
         """
         Filter the prediction features data and predict with it.
+        :param dk: dk: The datakitchen object
         :param unfiltered_df: Full dataframe for the current backtest period.
         :return:
         :pred_df: dataframe containing the predictions
@@ -78,7 +79,9 @@ class BasePyTorchClassifier(BasePyTorchModel):
         probs = F.softmax(logits, dim=-1)
         predicted_classes = torch.argmax(probs, dim=-1)
         predicted_classes_str = self.decode_class_names(predicted_classes)
-        pred_df_prob = DataFrame(probs.detach().numpy(), columns=class_names)
+        # used .tolist to convert probs into an iterable, in this way Tensors
+        # are automatically moved to the CPU first if necessary.
+        pred_df_prob = DataFrame(probs.detach().tolist(), columns=class_names)
         pred_df = DataFrame(predicted_classes_str, columns=[dk.label_list[0]])
         pred_df = pd.concat([pred_df, pred_df_prob], axis=1)
         return (pred_df, dk.do_predict)