diff --git a/src/brevitas/nn/quant_layer.py b/src/brevitas/nn/quant_layer.py
index ba4a474e2..8ec4be3aa 100644
--- a/src/brevitas/nn/quant_layer.py
+++ b/src/brevitas/nn/quant_layer.py
@@ -350,10 +350,9 @@ def forward_impl(self, inp: Union[Tensor, QuantTensor]) -> Union[Tensor, QuantTe
             output_tensor = self.inner_forward_impl(
                 _unpack_quant_tensor(quant_input), _unpack_quant_tensor(quant_weight), None)
 
-        if not self.is_output_quant_enabled:
+        if not self.is_output_quant_enabled and self.return_quant_tensor:
             if isinstance(quant_input, QuantTensor) and isinstance(quant_weight, QuantTensor):
-                if (quant_input.zero_point != 0.0
-                   ).any() or (quant_weight.zero_point != 0.0).any() and self.return_quant_tensor:
+                if (quant_input.zero_point != 0.0).any() or (quant_weight.zero_point != 0.0).any():
                     raise RuntimeError(
                         "Computing zero point of output accumulator not supported yet.")
                 elif output_zero_point is None:
diff --git a/tests/brevitas/nn/test_nn_quantizers.py b/tests/brevitas/nn/test_nn_quantizers.py
index b0db249af..bbee8daca 100644
--- a/tests/brevitas/nn/test_nn_quantizers.py
+++ b/tests/brevitas/nn/test_nn_quantizers.py
@@ -185,7 +185,13 @@ def test_quant_mha(model_input, current_cases):
         with pytest.raises(RuntimeError, match='Input scale required'):
             output, _ = model(inp, inp, inp)
         return
-
+    elif kwargs['weight_quant'] is not None and kwargs['io_quant'] is None:
+        if kwargs['weight_quant'] == 'quant_asym' and kwargs['return_quant_tensor']:
+            with pytest.raises(
+                    RuntimeError,
+                    match='Computing zero point of output accumulator not supported yet.'):
+                output, _ = model(inp, inp, inp)
+            return
     output, _ = model(inp, inp, inp)
 
     if kwargs['return_quant_tensor']: