diff --git a/src/brevitas/core/quant/int_base.py b/src/brevitas/core/quant/int_base.py
index 338e5a433..678b71009 100644
--- a/src/brevitas/core/quant/int_base.py
+++ b/src/brevitas/core/quant/int_base.py
@@ -8,7 +8,6 @@
 import brevitas
 from brevitas.core.function_wrapper import RoundSte
 from brevitas.core.function_wrapper import TensorClamp
-from brevitas.core.quant.delay import DelayWrapper
 from brevitas.function.ops import max_int
 from brevitas.function.ops import min_int
 
@@ -53,14 +52,12 @@ def __init__(
             signed: bool,
             input_view_impl: Module,
             float_to_int_impl: Module = RoundSte(),
-            tensor_clamp_impl: Module = TensorClamp(),
-            quant_delay_steps: int = 0):
+            tensor_clamp_impl: Module = TensorClamp()):
         super(IntQuant, self).__init__()
         self.float_to_int_impl = float_to_int_impl
         self.tensor_clamp_impl = tensor_clamp_impl
         self.signed = signed
         self.narrow_range = narrow_range
-        self.delay_wrapper = DelayWrapper(quant_delay_steps)
         self.input_view_impl = input_view_impl
 
     @brevitas.jit.script_method
@@ -87,7 +84,6 @@ def forward(self, scale: Tensor, zero_point: Tensor, bit_width: Tensor, x: Tenso
         y_int = self.to_int(scale, zero_point, bit_width, x)
         y = y_int - zero_point
         y = y * scale
-        y = self.delay_wrapper(x, y)
         return y
 
 
@@ -129,14 +125,12 @@ def __init__(
             signed: bool,
             input_view_impl: Module,
             float_to_int_impl: Module = RoundSte(),
-            tensor_clamp_impl: Module = TensorClamp(),
-            quant_delay_steps: int = 0):
+            tensor_clamp_impl: Module = TensorClamp()):
         super(DecoupledIntQuant, self).__init__()
         self.float_to_int_impl = float_to_int_impl
         self.tensor_clamp_impl = tensor_clamp_impl
         self.signed = signed
         self.narrow_range = narrow_range
-        self.delay_wrapper = DelayWrapper(quant_delay_steps)
         self.input_view_impl = input_view_impl
 
     @brevitas.jit.script_method
@@ -172,5 +166,4 @@ def forward(
         y_int = self.to_int(pre_scale, pre_zero_point, bit_width, x)
         y = y_int - zero_point
         y = y * scale
-        y = self.delay_wrapper(x, y)
         return y
diff --git a/src/brevitas/proxy/parameter_quant.py b/src/brevitas/proxy/parameter_quant.py
index f28233aed..96a2a05b6 100644
--- a/src/brevitas/proxy/parameter_quant.py
+++ b/src/brevitas/proxy/parameter_quant.py
@@ -16,6 +16,7 @@
 from brevitas import config
 from brevitas import is_dynamo_compiling
 from brevitas.core.function_wrapper.misc import Identity
+from brevitas.core.quant.delay import DelayWrapper
 from brevitas.function import max_int
 from brevitas.inject import BaseInjector as Injector
 from brevitas.quant_tensor import _unpack_quant_tensor
@@ -94,6 +95,7 @@ def __init__(self, quant_layer: nn.Module, quant_injector: Injector) -> None:
         self.cache_inference_quant_weight_metadata_only = False
         self.cache_class = None  # To be redefined by each class
         self.quant_tensor_class = None  # To be redefined by each class
+        self.delay_wrapper = DelayWrapper(quant_injector.quant_delay_steps)
 
     @property
     def input_view_impl(self):
@@ -136,7 +138,8 @@ def forward(self, x: torch.Tensor) -> Union[Tensor, QuantTensor]:
                 else:
                     out = self.create_quant_tensor(out)
             else:
-                out = self.tensor_quant(x)
+                quantized_out = self.tensor_quant(x)
+                out = self.delay_wrapper(x, quantized_out)
                 if is_dynamo_compiling():
                     out = out[0]
                 else:
diff --git a/tests/brevitas/proxy/test_proxy.py b/tests/brevitas/proxy/test_proxy.py
index 28c3eed9e..4f25bf2f4 100644
--- a/tests/brevitas/proxy/test_proxy.py
+++ b/tests/brevitas/proxy/test_proxy.py
@@ -80,3 +80,27 @@ def test_dynamic_act_proxy(self):
 
         model.act_quant.disable_quant = True
         assert model.act_quant.bit_width() is None
+
+    def test_delay_wrapper_in_weight_proxy(self):
+        model = QuantLinear(10, 5, weight_quant=Int8WeightPerTensorFloat)
+        assert model.weight_quant.delay_wrapper is not None
+
+        model.weight_quant.delay_wrapper.quant_delay_steps = 5
+        for _ in range(5):
+            quantized_out = model.weight_quant(model.weight)
+            assert torch.equal(quantized_out, model.weight)
+
+        quantized_out = model.weight_quant(model.weight)
+        assert not torch.equal(quantized_out, model.weight)
+
+    def test_delay_wrapper_in_bias_proxy(self):
+        model = QuantLinear(10, 5, bias_quant=Int8BiasPerTensorFloatInternalScaling)
+        assert model.bias_quant.delay_wrapper is not None
+
+        model.bias_quant.delay_wrapper.quant_delay_steps = 5
+        for _ in range(5):
+            quantized_out = model.bias_quant(model.bias)
+            assert torch.equal(quantized_out, model.bias)
+
+        quantized_out = model.bias_quant(model.bias)
+        assert not torch.equal(quantized_out, model.bias)