diff --git a/src/gluonts/model/forecast_generator.py b/src/gluonts/model/forecast_generator.py
index 42caf1fa9e..33b0320808 100644
--- a/src/gluonts/model/forecast_generator.py
+++ b/src/gluonts/model/forecast_generator.py
@@ -82,6 +82,15 @@ def make_distribution_forecast(distr, *args, **kwargs) -> Forecast:
     raise NotImplementedError
 
 
+def make_predictions(prediction_net, inputs: dict):
+    # MXNet predictors only support positional arguments
+    class_name = prediction_net.__class__.__module__
+    if class_name.startswith("gluonts.mx") or class_name.startswith("mxnet"):
+        return prediction_net(*inputs.values())
+    else:
+        return prediction_net(**inputs)
+
+
 class ForecastGenerator:
     """
     Classes used to bring the output of a network into a class.
@@ -115,7 +124,7 @@ def __call__(
     ) -> Iterator[Forecast]:
         for batch in inference_data_loader:
             inputs = select(input_names, batch, ignore_missing=True)
-            (outputs,), loc, scale = prediction_net(*inputs.values())
+            (outputs,), loc, scale = make_predictions(prediction_net, inputs)
             outputs = to_numpy(outputs)
             if scale is not None:
                 outputs = outputs * to_numpy(scale[..., None])
@@ -159,14 +168,16 @@ def __call__(
     ) -> Iterator[Forecast]:
         for batch in inference_data_loader:
             inputs = select(input_names, batch, ignore_missing=True)
-            outputs = to_numpy(prediction_net(*inputs.values()))
+            outputs = to_numpy(make_predictions(prediction_net, inputs))
             if output_transform is not None:
                 outputs = output_transform(batch, outputs)
             if num_samples:
                 num_collected_samples = outputs[0].shape[0]
                 collected_samples = [outputs]
                 while num_collected_samples < num_samples:
-                    outputs = to_numpy(prediction_net(*inputs.values()))
+                    outputs = to_numpy(
+                        make_predictions(prediction_net, inputs)
+                    )
                     if output_transform is not None:
                         outputs = output_transform(batch, outputs)
                     collected_samples.append(outputs)
@@ -209,7 +220,7 @@ def __call__(
     ) -> Iterator[Forecast]:
         for batch in inference_data_loader:
             inputs = select(input_names, batch, ignore_missing=True)
-            outputs = prediction_net(*inputs.values())
+            outputs = make_predictions(prediction_net, inputs)
 
             if output_transform:
                 log_once(OUTPUT_TRANSFORM_NOT_SUPPORTED_MSG)