diff --git a/src/brevitas/proxy/parameter_quant.py b/src/brevitas/proxy/parameter_quant.py
index f28233aed..2144edd53 100644
--- a/src/brevitas/proxy/parameter_quant.py
+++ b/src/brevitas/proxy/parameter_quant.py
@@ -23,6 +23,7 @@
 from brevitas.quant_tensor import QuantTensor
 from brevitas.utils.quant_utils import _CachedIO
 from brevitas.utils.torch_utils import compute_channel_view_shape
+from brevitas.utils.torch_utils import is_broadcastable
 
 from .quant_proxy import QuantProxyFromInjector
 from .quant_proxy import QuantProxyProtocol
@@ -309,7 +310,12 @@ def quant_output_scale_impl(
         channel_dim = -1 if isinstance(module, torch.nn.Linear) else 1
         output_scale_shape = compute_channel_view_shape(input, channel_dim=channel_dim)
         output_scale = weight.scale.view(output_scale_shape)
-        output_scale = output_scale * input.scale.view(output_scale_shape)
+
+        input_scale_view = input.scale.view(output_scale_shape)
+        if not is_broadcastable(output_scale.shape, input_scale_view.shape):
+            return None
+
+        output_scale = output_scale * input_scale_view
         return output_scale
 
     def compute_bias_scale(
@@ -336,8 +342,8 @@ def forward(
             weight: Optional[Union[Tensor,
                                    IntQuantTensor]] = None) -> Union[Tensor, IntQuantTensor]:
         out = x
-        input_scale = self.compute_bias_scale(input, weight)
         if self.is_quant_enabled:
+            input_scale = self.compute_bias_scale(input, weight)
             impl = self.export_handler if self.export_mode else self.tensor_quant
             if self.requires_input_scale and input_scale is None and self.is_quant_enabled:
                 input_scale = self.scale()
diff --git a/src/brevitas/quant_tensor/int_torch_handler.py b/src/brevitas/quant_tensor/int_torch_handler.py
index 8882bd097..3258b8914 100644
--- a/src/brevitas/quant_tensor/int_torch_handler.py
+++ b/src/brevitas/quant_tensor/int_torch_handler.py
@@ -10,6 +10,7 @@
 from brevitas.function.ops import max_int
 from brevitas.function.ops_ste import ceil_ste
 from brevitas.utils.torch_utils import compute_channel_view_shape
+from brevitas.utils.torch_utils import is_broadcastable
 
 INT_QUANT_TENSOR_FN_HANDLER = {}
 
@@ -198,6 +199,9 @@ def quant_layer(fn, quant_input, quant_weight, bias, *args, **kwargs):
                                                        (quant_weight.zero_point != 0.0).any()):
             warnings.warn("Computing zero point of output accumulator not supported yet.")
             compute_output_quant_tensor = False
+        if output_scale is None:
+            warnings.warn("Could not compute output scale factor, returning Tensor")
+            compute_output_quant_tensor = False
 
     if compute_output_quant_tensor:
         if output_zero_point is None:
@@ -230,8 +234,9 @@ def quant_output_scale_impl(
     output_scale_shape = compute_channel_view_shape(inp, channel_dim=channel_dim)
 
     quant_weight_scale = quant_weight_scale.view(output_scale_shape)
-    if len(quant_input_scale.shape) == 0:
-        quant_input_scale = quant_input_scale.view(output_scale_shape)
+    quant_input_scale = quant_input_scale.view(output_scale_shape)
+    if not is_broadcastable(quant_weight_scale.shape, quant_input_scale.shape):
+        return None
 
     output_scale = quant_weight_scale * quant_input_scale
     return output_scale
diff --git a/src/brevitas/utils/torch_utils.py b/src/brevitas/utils/torch_utils.py
index 2f0d34fba..8942c513a 100644
--- a/src/brevitas/utils/torch_utils.py
+++ b/src/brevitas/utils/torch_utils.py
@@ -113,3 +113,12 @@ def padding(x: torch.Tensor, group_size: int, group_dim: int) -> List[int]:
         padding[2 * group_dim] = group_size - size[group_dim] % group_size
     padding = list(reversed(padding))
     return padding
+
+
+def is_broadcastable(tensor, other):
+    for a, b in zip(tensor[::-1], other[::-1]):
+        if a == 1 or b == 1 or a == b:
+            pass
+        else:
+            return False
+    return True