diff --git a/src/brevitas/graph/gpfq.py b/src/brevitas/graph/gpfq.py
index 8ba6d2912..ef720d092 100644
--- a/src/brevitas/graph/gpfq.py
+++ b/src/brevitas/graph/gpfq.py
@@ -17,7 +17,6 @@
 from brevitas.graph.gpxq import StopFwdException
 from brevitas.graph.gpxq import SUPPORTED_CONV_OP
 import brevitas.nn as qnn
-from brevitas.quant_tensor import QuantTensor
 
 
 class gpfq_mode(gpxq_mode):
@@ -313,32 +312,10 @@ def __init__(
         self.accumulator_bit_width = accumulator_bit_width
         assert self.accumulator_bit_width is not None
 
-    def process_input(self, inp):
-        inp = super().process_input(inp)
-        inp = self.layer.input_quant(inp)
-
-        is_quant_enabled = self.layer.weight_quant.is_quant_enabled
-
-        # If using quantized activations, inp could be QuantTensor. In
-        # this case, we overwrite the metadata.
-        if isinstance(inp, QuantTensor):
-            if is_quant_enabled and self.quant_input is None:
-                self.quant_input = QuantTensor(
-                    value=torch.empty(
-                        1, dtype=self.layer.weight.dtype, device=self.layer.weight.device),
-                    scale=inp.scale,
-                    zero_point=inp.zero_point,
-                    bit_width=inp.bit_width,
-                    signed=inp.signed,
-                    training=inp.training)
-            inp = inp.value
-
-        return inp
-
     def single_layer_update(self):
         # raise error in case no quant-input is here
-        if self.quant_input is None:
-            raise ValueError('Expected self.quant_input to calculate L1-norm upper bound, but recevied None. ' + \
+        if self.quant_metadata is None:
+            raise ValueError('Expected self.quant_metadata to calculate L1-norm upper bound, but recevied None. ' + \
                 'Make sure that either the input to the model is a QuantTensor or the layer has an input quant enabled. ' \
                 'Also, check if `use_quant_activations=True` in `gpfq_mode` when `accumulator_bit_width` is specified. ' + \
                 'Alternatively, provide a custom `a2q_layer_filter_fnc` to `gpfq_mode` to filter layers without a quant_tensor input.')
@@ -356,8 +333,8 @@ def single_layer_update(self):
         self.quantized_input = self.quantized_input.to(dev)
 
         # get upper bound
-        input_bit_width = self.quant_input.bit_width
-        input_is_signed = self.quant_input.signed
+        input_bit_width = self.quant_metadata.bit_width
+        input_is_signed = self.quant_metadata.signed
         T = get_upper_bound_on_l1_norm(
             torch.tensor(self.accumulator_bit_width), input_bit_width, input_is_signed)
         s = self.layer.weight_quant.scale()
diff --git a/src/brevitas/graph/gpxq.py b/src/brevitas/graph/gpxq.py
index aa174f5a3..c38a15712 100644
--- a/src/brevitas/graph/gpxq.py
+++ b/src/brevitas/graph/gpxq.py
@@ -18,6 +18,8 @@
 from brevitas.graph.calibrate import DisableEnableQuantization
 from brevitas.graph.calibrate import restore_return_quant_tensor
 import brevitas.nn as qnn
+from brevitas.quant_tensor import QuantTensor
+from brevitas.utils.quant_utils import _CachedIO
 
 SUPPORTED_CONV_OP = (
     qnn.QuantConv1d,
@@ -216,11 +218,21 @@ def __init__(
 
         self.disable_pre_forward_hook = False
         # Some layers require knowledge from quant inputs to compute quant weights
-        self.quant_input = None
+        self.quant_metadata = None
 
     def process_input(self, inp):
         # Input is a tuple, so we take first element
         inp = inp[0]
+        inp = self.layer.input_quant(inp)
+
+        is_quant_enabled = self.layer.weight_quant.is_quant_enabled
+
+        # If using quantized activations, inp could be QuantTensor. In
+        # this case, we overwrite the metadata.
+        if isinstance(inp, QuantTensor):
+            if is_quant_enabled and self.quant_metadata is None:
+                self.quant_metadata = _CachedIO(inp, metadata_only=True)
+            inp = inp.value
 
         # If input is unbatched, add batch_size = 1
         if len(inp.shape) == 1:
@@ -232,6 +244,7 @@ def process_input(self, inp):
             batch_dim = inp.names.index('N')
             inp.rename_(None)
             inp = inp.transpose(0, batch_dim)
+
         return inp
 
     @abstractmethod