diff --git a/src/brevitas/nn/mixin/base.py b/src/brevitas/nn/mixin/base.py
index 8810e2af2..a70394e07 100644
--- a/src/brevitas/nn/mixin/base.py
+++ b/src/brevitas/nn/mixin/base.py
@@ -147,14 +147,9 @@ def state_dict(self, destination=None, prefix='', keep_vars=False):
 
     @staticmethod
     def gate_params_fwd(gate, quant_input):
-        acc_scale = None
         quant_weight_ih = gate.input_weight()
         quant_weight_hh = gate.hidden_weight()
-        if isinstance(quant_input, QuantTensor) and isinstance(quant_weight_ih, QuantTensor):
-            acc_scale_shape = compute_channel_view_shape(quant_input.value, channel_dim=1)
-            acc_scale = quant_weight_ih.scale.view(acc_scale_shape)
-            acc_scale = acc_scale * quant_input.scale.view(acc_scale_shape)
-        quant_bias = gate.bias_quant(gate.bias, acc_scale)
+        quant_bias = gate.bias_quant(gate.bias, quant_input, quant_weight_ih)
         return quant_weight_ih, quant_weight_hh, quant_bias
 
     def reset_parameters(self) -> None:
diff --git a/src/brevitas/nn/quant_layer.py b/src/brevitas/nn/quant_layer.py
index 0a82afb9b..cd5f48418 100644
--- a/src/brevitas/nn/quant_layer.py
+++ b/src/brevitas/nn/quant_layer.py
@@ -118,14 +118,6 @@ def inner_forward_impl(self, x: Tensor, quant_weight: Tensor, quant_bias: Option
     def max_acc_bit_width(self, input_bit_width: Tensor, quant_weight_bit_width: Tensor):
         pass
 
-    def quant_output_scale_impl(
-            self, inp: Tensor, quant_input_scale: Tensor, quant_weight_scale: Tensor):
-        channel_dim = -1 if isinstance(self, torch.nn.Linear) else 1
-        output_scale_shape = compute_channel_view_shape(inp, channel_dim=channel_dim)
-        output_scale = quant_weight_scale.view(output_scale_shape)
-        output_scale = output_scale * quant_input_scale.view(output_scale_shape)
-        return output_scale
-
     @property
     def requires_export_handler(self):
         return (
@@ -150,7 +142,6 @@ def forward_impl(self, inp: Union[Tensor, QuantTensor]) -> Union[Tensor, QuantTe
             return out
 
         quant_input = self.input_quant(inp)
-
         quant_weight = self.quant_weight(quant_input)
 
         compute_output_quant_tensor = isinstance(quant_input, QuantTensor) and isinstance(
@@ -159,12 +150,8 @@ def forward_impl(self, inp: Union[Tensor, QuantTensor]) -> Union[Tensor, QuantTe
                 self.output_quant.is_quant_enabled) and self.return_quant_tensor:
             raise RuntimeError("QuantLayer is not correctly configured")
 
-        output_scale = None
-        if isinstance(quant_input, QuantTensor) and isinstance(quant_weight, QuantTensor):
-            output_scale = self.quant_output_scale_impl(inp, quant_input.scale, quant_weight.scale)
-
         if self.bias is not None:
-            quant_bias = self.bias_quant(self.bias, output_scale)
+            quant_bias = self.bias_quant(self.bias, quant_input, quant_weight)
         else:
             quant_bias = None
         output_tensor = self.inner_forward_impl(quant_input, quant_weight, quant_bias)
diff --git a/src/brevitas/proxy/parameter_quant.py b/src/brevitas/proxy/parameter_quant.py
index 893ff5e30..4a95b5e0f 100644
--- a/src/brevitas/proxy/parameter_quant.py
+++ b/src/brevitas/proxy/parameter_quant.py
@@ -17,6 +17,7 @@
 from brevitas.inject import BaseInjector as Injector
 from brevitas.quant_tensor import QuantTensor
 from brevitas.utils.quant_utils import _CachedIO
+from brevitas.utils.torch_utils import compute_channel_view_shape
 
 from .quant_proxy import QuantProxyFromInjector
 from .quant_proxy import QuantProxyProtocol
@@ -234,10 +235,38 @@ def bit_width(self):
         bit_width = self.__call__(self.tracked_parameter_list[0], zhs).bit_width
         return bit_width
 
-    def forward(self,
-                x: Tensor,
-                input_scale: Optional[Tensor] = None) -> Union[Tensor, QuantTensor]:
+    def quant_output_scale_impl(
+            self, input: QuantTensor, weight: QuantTensor, module: torch.nn.Module) -> Tensor:
+        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)
+        return output_scale
+
+    def compute_bias_scale(
+            self,
+            input: Optional[Union[Tensor, QuantTensor]],
+            weight: Optional[Union[Tensor, QuantTensor]]) -> Optional[Tensor]:
+        if not self.requires_input_scale:
+            return None
+        if not isinstance(input, QuantTensor) or not isinstance(weight, QuantTensor):
+            return None
+        if len(self.tracked_module_list) > 1:
+            if not all(
+                [type[self.tracked_module_list[0]] == type[x] for x in self.tracked_module_list]):
+                raise RuntimeError(
+                    "Bias quantizer shared across different type of layers with external scale is not supported."
+                )
+        scale = self.quant_output_scale_impl(input, weight, self.tracked_module_list[0])
+        return scale
+
+    def forward(
+            self,
+            x: Tensor,
+            input: Optional[Union[Tensor, QuantTensor]] = None,
+            weight: Optional[Union[Tensor, QuantTensor]] = None) -> Union[Tensor, QuantTensor]:
         out = x
+        input_scale = self.compute_bias_scale(input, weight)
         if self.is_quant_enabled:
             impl = self.export_handler if self.export_mode else self.tensor_quant
             if self.requires_input_scale and input_scale is None: