diff --git a/freqtrade/freqai/base_models/BasePyTorchRegressor.py b/freqtrade/freqai/base_models/BasePyTorchRegressor.py
index b9c5fa685..ea6fabe49 100644
--- a/freqtrade/freqai/base_models/BasePyTorchRegressor.py
+++ b/freqtrade/freqai/base_models/BasePyTorchRegressor.py
@@ -45,5 +45,6 @@ class BasePyTorchRegressor(BasePyTorchModel):
             device=self.device
         )
         y = self.model.model(x)
+        y = y.cpu()
         pred_df = DataFrame(y.detach().numpy(), columns=[dk.label_list[0]])
         return (pred_df, dk.do_predict)
diff --git a/freqtrade/freqai/torch/PyTorchModelTrainer.py b/freqtrade/freqai/torch/PyTorchModelTrainer.py
index 9c1a1cb6e..8277ba937 100644
--- a/freqtrade/freqai/torch/PyTorchModelTrainer.py
+++ b/freqtrade/freqai/torch/PyTorchModelTrainer.py
@@ -143,8 +143,8 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
         """
         data_loader_dictionary = {}
         for split in splits:
-            x = self.data_convertor.convert_x(data_dictionary[f"{split}_features"])
-            y = self.data_convertor.convert_y(data_dictionary[f"{split}_labels"])
+            x = self.data_convertor.convert_x(data_dictionary[f"{split}_features"], self.device)
+            y = self.data_convertor.convert_y(data_dictionary[f"{split}_labels"], self.device)
             dataset = TensorDataset(*x, *y)
             data_loader = DataLoader(
                 dataset,