diff --git a/tests/tpu/test_compilation.py b/tests/tpu/test_compilation.py
index 5a432fb78b3d..d8df86b2aaa1 100644
--- a/tests/tpu/test_compilation.py
+++ b/tests/tpu/test_compilation.py
@@ -5,6 +5,10 @@
 
 import depyf
 
+# disable custom dispatcher, let Dynamo takes over
+# all the control
+os.environ['VLLM_DYNAMO_USE_CUSTOM_DISPATCHER'] = "0"
+
 temp_dir = tempfile.mkdtemp()
 with depyf.prepare_debug(temp_dir):
     cur_dir = os.path.dirname(__file__)
@@ -16,19 +20,36 @@
 
 compiled_code = sorted(
     glob.glob(os.path.join(temp_dir, "__transformed_code*.py")))
-full_code = glob.glob(os.path.join(temp_dir, "full_code*.py"))[0]
+
 # we should only trigger Dynamo compilation three times:
-# one for the profiling phase (and the compiled artifact will be discarded)
+# one for the profiling phase without kv cache
 # one for the prefill phase with symbolic shapes
 # one for the decode phase with symbolic shapes
 # and later calls should not trigger Dynamo compilation again.
 # NOTE: it might still trigger XLA compilation.
 
 # check we have three compiled code
+# this is the assumption when we use the custom dispatcher
 assert len(compiled_code) == 3
 
-# check the first compilation is discarded
-with open(full_code) as f:
-    full_code_content = f.read()
-    profile_function = compiled_code[0].split(".")[0]
-    assert profile_function not in full_code_content
+# check all the compilations are as expected
+compiled_fn = sorted(
+    glob.glob(os.path.join(temp_dir, "__compiled_fn*Captured*.py")))
+
+# the first compilation is the profiling phase,
+# it should not have any kv cache
+with open(compiled_fn[0]) as f:
+    content = f.read()
+    assert "kv_caches" not in content
+
+# the second compilation is the prefill phase,
+# it should have kv cache and the flash_attention op
+with open(compiled_fn[1]) as f:
+    content = f.read()
+    assert "kv_caches" in content and "torch.ops.xla.flash_attention" in content
+
+# the third compilation is the decode phase,
+# it should have kv cache and the paged_attention op
+with open(compiled_fn[2]) as f:
+    content = f.read()
+    assert "kv_caches" in content and "torch.ops.xla.paged_attention" in content
diff --git a/vllm/worker/model_runner.py b/vllm/worker/model_runner.py
index 2b287a5d2715..de1a2e3235a8 100644
--- a/vllm/worker/model_runner.py
+++ b/vllm/worker/model_runner.py
@@ -1123,10 +1123,6 @@ def profile_run(self) -> None:
                 device=self.device)
         self.execute_model(model_input, kv_caches, intermediate_tensors)
         torch.cuda.synchronize()
-
-        # reset and discard the guard and compiled bytecode for profiling runs
-        torch._dynamo.reset()
-
         return
 
     def remove_all_loras(self):
diff --git a/vllm/worker/tpu_worker.py b/vllm/worker/tpu_worker.py
index 320b15d3604b..44fa3aed5816 100644
--- a/vllm/worker/tpu_worker.py
+++ b/vllm/worker/tpu_worker.py
@@ -143,10 +143,6 @@ def determine_num_available_blocks(self) -> Tuple[int, int]:
         num_cpu_blocks = int(self.cache_config.swap_space_bytes //
                              block_size_bytes)
         num_cpu_blocks = (num_cpu_blocks // 8) * 8  # Round down to 8.
-
-        # reset and discard the guard and compiled bytecode for profiling runs
-        torch._dynamo.reset()
-
         return num_tpu_blocks, num_cpu_blocks
 
     def initialize_cache(