diff --git a/src/brevitas/graph/quantize.py b/src/brevitas/graph/quantize.py
index 4584a4eb8..9be9fb812 100644
--- a/src/brevitas/graph/quantize.py
+++ b/src/brevitas/graph/quantize.py
@@ -268,6 +268,7 @@ def preprocess_for_quantize(
         channel_splitting=False,
         channel_splitting_ratio=0.02,
         channel_splitting_grid_aware=False,
+        channel_splitting_split_iteratively=False,
         channel_splitting_criterion: str = 'maxabs',
         channel_splitting_weight_bit_width: int = 8):
 
@@ -296,6 +297,7 @@ def preprocess_for_quantize(
             split_ratio=channel_splitting_ratio,
             grid_aware=channel_splitting_grid_aware,
             split_criterion=channel_splitting_criterion,
+            split_iteratively=channel_splitting_split_iteratively,
             weight_bit_width=channel_splitting_weight_bit_width).apply(model)
     model.train(training_state)
     return model
diff --git a/src/brevitas/ptq_algorithms/channel_splitting.py b/src/brevitas/ptq_algorithms/channel_splitting.py
index 7189e79be..a752b24b6 100644
--- a/src/brevitas/ptq_algorithms/channel_splitting.py
+++ b/src/brevitas/ptq_algorithms/channel_splitting.py
@@ -392,13 +392,19 @@ def split_channels_iteratively(module, split_ratio, grid_aware, bit_width):
 class LayerwiseChannelSplitting(GraphTransform):
 
     def __init__(
-            self, split_ratio=0.02, split_criterion='maxabs', grid_aware=False, weight_bit_width=8):
+            self,
+            split_ratio=0.02,
+            split_criterion='maxabs',
+            grid_aware=False,
+            split_iteratively=False,
+            weight_bit_width=8):
         super(LayerwiseChannelSplitting, self).__init__()
 
         self.grid_aware = grid_aware
         self.split_ratio = split_ratio
         self.split_criterion = split_criterion
         self.weight_bit_width = weight_bit_width
+        self.split_iteratively = split_iteratively
 
     def _is_supported_module(self, graph_model: GraphModule, node: Node) -> bool:
         if node.op == 'call_module':
@@ -413,16 +419,26 @@ def apply(self, graph_model: GraphModule):
         for node in graph_model.graph.nodes:
             if self._is_supported_module(graph_model, node):
                 module = get_module(graph_model, node.target)
-                # we only split input channels
-                # channels_to_split = _channels_to_split({}, {node.target: module}, split_ratio=self.split_ratio, split_input=True, split_criterion=self.split_criterion)
-                # split the channels in the module
-                # _split_channels(module, channels_to_split, grid_aware=self.grid_aware, split_input=True, bit_width=self.weight_bit_width)
+                if self.split_iteratively:
+                    channels_to_split = split_channels_iteratively(
+                        module,
+                        split_ratio=self.split_ratio,
+                        grid_aware=self.grid_aware,
+                        bit_width=self.weight_bit_width)
+                else:
+                    # we only split input channels
+                    channels_to_split = _channels_to_split({}, {node.target: module},
+                                                           split_ratio=self.split_ratio,
+                                                           split_input=True,
+                                                           split_criterion=self.split_criterion)
+                    # split the channels in the module
+                    _split_channels(
+                        module,
+                        channels_to_split,
+                        grid_aware=self.grid_aware,
+                        split_input=True,
+                        bit_width=self.weight_bit_width)
                 # add node to split modules
-                channels_to_split = split_channels_iteratively(
-                    module,
-                    split_ratio=self.split_ratio,
-                    grid_aware=self.grid_aware,
-                    bit_width=self.weight_bit_width)
                 split_modules[module] = torch.tensor(channels_to_split)
 
         for module, channels_to_split in split_modules.items():
diff --git a/src/brevitas_examples/imagenet_classification/ptq/ptq_evaluate.py b/src/brevitas_examples/imagenet_classification/ptq/ptq_evaluate.py
index 4e2262eb0..08c0636b7 100644
--- a/src/brevitas_examples/imagenet_classification/ptq/ptq_evaluate.py
+++ b/src/brevitas_examples/imagenet_classification/ptq/ptq_evaluate.py
@@ -242,6 +242,13 @@
     'split-input',
     default=False,
     help='Input Channels Splitting for channel splitting (default: disabled)')
+add_bool_arg(
+    parser,
+    'split-iteratively',
+    default=False,
+    help=
+    'Input Channels are split iteratively, allows the same channel to be split multiple times (default: disabled)'
+)
 
 
 def main():
@@ -313,6 +320,7 @@ def main():
         f"Channel Splitting: {args.channel_splitting} - "
         f"Split Ratio: {args.split_ratio} - "
         f"Grid Aware: {args.grid_aware} - "
+        f"Split Iteratively: {args.split_iteratively} - "
         f"Merge BN: {not args.calibrate_bn}")
 
     # Get model-specific configurations about input shapes and normalization
@@ -360,6 +368,7 @@ def main():
             merge_bn=not args.calibrate_bn,
             channel_splitting=args.channel_splitting,
             channel_splitting_grid_aware=args.grid_aware,
+            channel_splitting_split_iteratively=args.split_iteratively,
             channel_splitting_ratio=args.split_ratio,
             channel_splitting_weight_bit_width=args.weight_bit_width)
     else: