diff --git a/src/brevitas/proxy/parameter_quant.py b/src/brevitas/proxy/parameter_quant.py
index 06e181f31..fc4e75cb9 100644
--- a/src/brevitas/proxy/parameter_quant.py
+++ b/src/brevitas/proxy/parameter_quant.py
@@ -124,6 +124,11 @@ def get_cached(self, attr):
 
 class WeightQuantProxyFromInjector(WeightQuantProxyFromInjectorBase):
 
+    def __init__(self, quant_layer: nn.Module, quant_injector: Injector) -> None:
+        super().__init__(quant_layer, quant_injector)
+        self._cached_weight = None
+        self.cache_inference_quant_weight = False
+
     @property
     def tracked_parameter_list(self):
         return [m.weight for m in self.tracked_module_list if m.weight is not None]
@@ -152,11 +157,20 @@ def bit_width(self):
 
     def forward(self, x: torch.Tensor) -> Union[Tensor, IntQuantTensor]:
         if self.is_quant_enabled:
-            impl = self.export_handler if self.export_mode else self.tensor_quant
-            out, scale, zero_point, bit_width = impl(x)
-            return IntQuantTensor(out, scale, zero_point, bit_width, self.is_signed, self.training)
+            if self._cached_weight is not None:
+                out = self._cached_weight.quant_tensor
+            else:
+                impl = self.export_handler if self.export_mode else self.tensor_quant
+                out, scale, zero_point, bit_width = impl(x)
+                out = IntQuantTensor(
+                    out, scale, zero_point, bit_width, self.is_signed, self.training)
         else:  # quantization disabled
-            return x
+            out = x
+        if isinstance(
+                out, IntQuantTensor
+        ) and not self.training and self.cache_inference_quant_weight and self._cached_weight is None:
+            self._cached_weight = _CachedIO(out.detach(), metadata_only=False)
+        return out
 
 
 class DecoupledWeightQuantProxyFromInjector(WeightQuantProxyFromInjector):
@@ -232,32 +246,6 @@ def forward(
 
 class BiasQuantProxyFromInjector(BiasQuantProxyFromInjectorBase):
 
-    def __init__(self, quant_layer: nn.Module, quant_injector: Injector) -> None:
-        super().__init__(quant_layer, quant_injector)
-        self._cached_bias = None
-        self.cache_inference_quant_bias = False
-
-    @property
-    def tracked_parameter_list(self):
-        return [m.bias for m in self.tracked_module_list if m.bias is not None]
-
-    @property
-    def requires_input_scale(self) -> bool:
-        if self.is_quant_enabled:
-            return self.quant_injector.requires_input_scale
-        else:
-            return False
-
-    def get_cached(self, attr):
-        if self._cached_bias is None:
-            warn(
-                "No quant bias cache found, set cache_inference_quant_bias=True and run an "
-                "inference pass first")
-            return None
-        if self.training:
-            warn("Cached quant bias scale is being used in training mode.")
-        return getattr(self._cached_bias, attr)
-
     def scale(self):
         if not self.is_quant_enabled:
             return None