diff --git a/tt_torch/dynamo/backend.py b/tt_torch/dynamo/backend.py
index 6a1f0a51..603361fb 100644
--- a/tt_torch/dynamo/backend.py
+++ b/tt_torch/dynamo/backend.py
@@ -263,9 +263,11 @@ def run_gm_op_by_op(self, *inputs):
         input_index = 0
         outputs = []
         num_nodes = len(self.gm.graph.nodes)
+        node_to_users = {}
         for idx, node in enumerate(self.gm.graph.nodes):
             print(f"Compiling {idx}/{num_nodes}: {node.target}")
             if node.op == "placeholder":
+                node_to_users[node] = len(node.users)
                 node_to_tensor[node] = inputs[input_index]
                 input_index += 1
             elif node.op == "get_attr":
@@ -274,6 +276,7 @@ def run_gm_op_by_op(self, *inputs):
                         node_to_tensor[node] = buffer[1]
                         break
             elif node.op == "call_function":
+                node_to_users[node] = len(node.users)
                 args = []
                 for arg in node.args:
                     if isinstance(arg, torch.fx.node.Node):
@@ -305,11 +308,31 @@ def run_gm_op_by_op(self, *inputs):
                     tensor = node.target(*args, **node.kwargs)
 
                 node_to_tensor[node] = tensor
+                args_set = set()
+                for arg in node.args:
+                    if arg in args_set:  # skip duplicate args
+                        continue
+                    args_set.add(arg)
+                    if isinstance(arg, torch.fx.node.Node):
+                        node_to_users[arg] -= 1
             elif node.op == "output":
                 args = node.args[0]
                 output_tensors = [node_to_tensor[arg] for arg in args]
                 outputs = output_tensors
 
+                args_set = set()
+                for arg in args:
+                    if arg in args_set:
+                        continue
+                    args_set.add(arg)
+                    if isinstance(arg, torch.fx.node.Node):
+                        node_to_users[arg] -= 1
+        for arg in reversed(list(node_to_users.keys())):
+            if node.name == "output":
+                continue
+            if node_to_users[arg] == 0:
+                del node_to_tensor[arg]
+                self.gm.graph.erase_node(arg)
         self.compiler_config.save_unique_ops()
         return outputs