diff --git a/backends/vulkan/_passes/tag_memory_meta_pass.py b/backends/vulkan/_passes/tag_memory_meta_pass.py
index 0f8263fa572..6f5cb10f1b2 100644
--- a/backends/vulkan/_passes/tag_memory_meta_pass.py
+++ b/backends/vulkan/_passes/tag_memory_meta_pass.py
@@ -28,11 +28,13 @@ def insert_transition_node(
     node: torch.fx.Node,
     arg: torch.fx.Node,
     arg_node_repr: utils.TensorRepr,
-) -> None:
+) -> torch.fx.Node:
     """
     Insert a clone node to transition the tensor associated with `arg` to a tensor with
     the requested representation `arg_node_repr`, and use the cloned node as an argument
     to `node` instead of `arg`.
+
+    Returns the newly created clone node.
     """
     with graph_module.graph.inserting_before(node):
         clone_node = graph_module.graph.create_node(
@@ -45,6 +47,7 @@ def insert_transition_node(
         clone_node.meta["spec"].const = False
         utils.set_node_repr(clone_node, arg_node_repr)
         arg.replace_all_uses_with(clone_node, lambda x, y=node: x == y)
+    return clone_node
 
 
 def set_arg_node_repr_or_transition(
@@ -53,6 +56,7 @@ def set_arg_node_repr_or_transition(
     arg_i: int,
     arg_node_repr: utils.TensorRepr,
     dirty: bool,
+    transition_cache: dict | None = None,
 ) -> bool:
     """
     Does one of following:
@@ -60,7 +64,8 @@ def set_arg_node_repr_or_transition(
        does not currently have a `node_repr`
     2. No-op if the current `node_repr` is already the same as the requested represetnation.
     3. Insert a transition node to create a copy of the argument with the desired `node_repr`
-       if the current `node_repr` is different than what is needed.
+       if the current `node_repr` is different than what is needed. If a transition clone
+       already exists for the same (source, target_repr) pair, reuse it.
     """
     arg_node = op_node.args[arg_i]
 
@@ -78,15 +83,33 @@ def single_node_impl(node: torch.fx.Node) -> bool:
         if cur_node_repr == arg_node_repr:
             return False
 
+        assert utils.is_single_tensor_node(node)
+
+        # Check if a transition clone already exists for this (source, target_repr).
+        cache_key = (
+            node,
+            arg_node_repr.storage_type,
+            arg_node_repr.memory_layout,
+        )
+        if transition_cache is not None and cache_key in transition_cache:
+            cached_clone = transition_cache[cache_key]
+            node.replace_all_uses_with(cached_clone, lambda x, y=op_node: x == y)
+            if not dirty:
+                logger.info(
+                    f"[Vulkan Delegate] Reusing transition for {op_node.format_node()}:"
+                )
+            logger.info(f"   arg {arg_i} ({node}): reusing {cached_clone}")
+            return True
+
         if not dirty:
             logger.info(
                 f"[Vulkan Delegate] Inserting transition(s) for {op_node.format_node()}:"
             )
 
-        # Existing node representation is different; insert a transition node
-        # Currently, the transition node insertion logic can only handle single tensor nodes
-        assert utils.is_single_tensor_node(node)
-        insert_transition_node(graph_module, op_node, node, arg_node_repr)
+        clone_node = insert_transition_node(graph_module, op_node, node, arg_node_repr)
+
+        if transition_cache is not None:
+            transition_cache[cache_key] = clone_node
 
         logger.info(f"   arg {arg_i} ({node}): ({cur_node_repr}) -> ({arg_node_repr})")
 
@@ -407,7 +430,10 @@ def constrain_op_repsets(self, op_repsets: utils.OpRepSets) -> None:
         self.constrain_op_out_repset(op_repsets)
 
     def set_op_node_tensor_reprs(
-        self, graph_module: torch.fx.GraphModule, op_node: torch.fx.Node
+        self,
+        graph_module: torch.fx.GraphModule,
+        op_node: torch.fx.Node,
+        transition_cache: dict | None = None,
     ) -> None:
         """
         For an operator representated by `op_node`, get the OpRepSets associated with
@@ -458,7 +484,12 @@ def set_op_node_tensor_reprs(
             if isinstance(arg_node, torch.fx.Node):
                 transitions_inserted = (
                     set_arg_node_repr_or_transition(
-                        graph_module, op_node, i, arg_node_repr, transitions_inserted
+                        graph_module,
+                        op_node,
+                        i,
+                        arg_node_repr,
+                        transitions_inserted,
+                        transition_cache,
                     )
                     or transitions_inserted
                 )
@@ -473,12 +504,14 @@ def set_op_node_tensor_reprs(
                             i,
                             arg_node_repr,
                             transitions_inserted,
+                            transition_cache,
                         )
                         or transitions_inserted
                     )
 
     def call(self, graph_module: torch.fx.GraphModule) -> PassResult:
+        transition_cache: dict = {}
         for node in graph_module.graph.nodes:
-            self.set_op_node_tensor_reprs(graph_module, node)
+            self.set_op_node_tensor_reprs(graph_module, node, transition_cache)
 
         return PassResult(graph_module, True)