diff --git a/src/brevitas/proxy/parameter_quant.py b/src/brevitas/proxy/parameter_quant.py
index ec533351a..5cf1e0372 100644
--- a/src/brevitas/proxy/parameter_quant.py
+++ b/src/brevitas/proxy/parameter_quant.py
@@ -335,29 +335,25 @@ def forward(
         out = x
         input_scale = self.compute_bias_scale(input, weight)
         if self.is_quant_enabled:
-            if self._cached_bias is not None and not self.cache_inference_quant_bias_metadata_only:
-                out = self._cached_bias.value
+            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()
+                if input_scale is None:
+                    raise RuntimeError("Input scale required")
+            elif self.requires_input_scale and input_scale is not None and self.is_quant_enabled:
+                input_scale = input_scale.view(-1)
+
+            if self.requires_input_scale and self.is_quant_enabled:
+                out, out_scale, out_zp, out_bit_width = impl(x, input_scale)
             else:
-                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()
-                    if input_scale is None:
-                        raise RuntimeError("Input scale required")
-                elif self.requires_input_scale and input_scale is not None and self.is_quant_enabled:
-                    input_scale = input_scale.view(-1)
-
-                if self.requires_input_scale and self.is_quant_enabled:
-                    out, out_scale, out_zp, out_bit_width = impl(x, input_scale)
-                else:
-                    out, out_scale, out_zp, out_bit_width = impl(x)
-                if not is_dynamo_compiling():
-                    out = IntQuantTensor(
-                        out, out_scale, out_zp, out_bit_width, self.is_signed, self.training)
-                    if not self.training and self.cache_inference_quant_bias and self._cached_bias is not None:
-                        cached_bias = _CachedIO(
-                            out.detach(),
-                            metadata_only=self.cache_inference_quant_bias_metadata_only)
-                        self._cached_bias = cached_bias
+                out, out_scale, out_zp, out_bit_width = impl(x)
+            if not is_dynamo_compiling():
+                out = IntQuantTensor(
+                    out, out_scale, out_zp, out_bit_width, self.is_signed, self.training)
+                if not self.training and self.cache_inference_quant_bias:
+                    cached_bias = _CachedIO(
+                        out.detach(), metadata_only=self.cache_inference_quant_bias_metadata_only)
+                    self._cached_bias = cached_bias
         else:
             out = x
         return out