diff --git a/.buildkite/run-tpu-v1-test.sh b/.buildkite/run-tpu-v1-test.sh
index 82f40c650f8cf..6562942ea3f8c 100755
--- a/.buildkite/run-tpu-v1-test.sh
+++ b/.buildkite/run-tpu-v1-test.sh
@@ -19,17 +19,19 @@ docker run --privileged --net host --shm-size=16G -it \
     vllm-tpu /bin/bash -c "python3 -m pip install git+https://github.com/thuml/depyf.git \
     && python3 -m pip install pytest \
     && python3 -m pip install lm_eval[api]==0.4.4 \
+    && export VLLM_USE_V1=1 \
+    && export VLLM_XLA_CHECK_RECOMPILATION=1 \
     && echo TEST_1 \
-    && VLLM_USE_V1=1 python3 /workspace/vllm/tests/tpu/test_compilation.py \
+    && python3 /workspace/vllm/tests/tpu/test_compilation.py \
     && echo TEST_2 \
-    && VLLM_USE_V1=1 pytest -v -s /workspace/vllm/tests/v1/tpu/test_basic.py \
+    && pytest -v -s /workspace/vllm/tests/v1/tpu/test_basic.py \
     && echo TEST_3 \
-    && VLLM_USE_V1=1 pytest -v -s /workspace/vllm/tests/entrypoints/llm/test_accuracy.py::test_lm_eval_accuracy_v1_engine \
+    && pytest -v -s /workspace/vllm/tests/entrypoints/llm/test_accuracy.py::test_lm_eval_accuracy_v1_engine \
     && echo TEST_4 \
-    && VLLM_USE_V1=1 pytest -s -v /workspace/vllm/tests/tpu/test_quantization_accuracy.py \
+    && pytest -s -v /workspace/vllm/tests/tpu/test_quantization_accuracy.py \
     && echo TEST_5 \
-    && VLLM_USE_V1=1 python3 /workspace/vllm/examples/offline_inference/tpu.py" \
-    
+    && python3 /workspace/vllm/examples/offline_inference/tpu.py" \
+
 
 # TODO: This test fails because it uses RANDOM_SEED sampling
 # && VLLM_USE_V1=1 pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py \
diff --git a/vllm/envs.py b/vllm/envs.py
index d88ab3b5e7d06..d54de9da25315 100644
--- a/vllm/envs.py
+++ b/vllm/envs.py
@@ -45,6 +45,7 @@ if TYPE_CHECKING:
     VLLM_OPENVINO_CPU_KV_CACHE_PRECISION: Optional[str] = None
     VLLM_OPENVINO_ENABLE_QUANTIZED_WEIGHTS: bool = False
     VLLM_XLA_CACHE_PATH: str = os.path.join(VLLM_CACHE_ROOT, "xla_cache")
+    VLLM_XLA_CHECK_RECOMPILATION: bool = False
     VLLM_FUSED_MOE_CHUNK_SIZE: int = 64 * 1024
     VLLM_USE_RAY_SPMD_WORKER: bool = False
     VLLM_USE_RAY_COMPILED_DAG: bool = False
@@ -446,6 +447,10 @@ environment_variables: dict[str, Callable[[], Any]] = {
             "VLLM_XLA_CACHE_PATH",
             os.path.join(get_default_cache_root(), "vllm", "xla_cache"),
         )),
+
+    # If set, assert on XLA recompilation after each execution step.
+    "VLLM_XLA_CHECK_RECOMPILATION":
+    lambda: bool(int(os.getenv("VLLM_XLA_CHECK_RECOMPILATION", "0"))),
     "VLLM_FUSED_MOE_CHUNK_SIZE":
     lambda: int(os.getenv("VLLM_FUSED_MOE_CHUNK_SIZE", "32768")),
 
diff --git a/vllm/v1/worker/tpu_model_runner.py b/vllm/v1/worker/tpu_model_runner.py
index ec3dcbc064cba..d772a3ee13ec3 100644
--- a/vllm/v1/worker/tpu_model_runner.py
+++ b/vllm/v1/worker/tpu_model_runner.py
@@ -11,6 +11,7 @@ import torch.nn as nn
 import torch_xla.core.xla_model as xm
 import torch_xla.runtime as xr
 
+import vllm.envs as envs
 from vllm.attention.backends.abstract import AttentionType
 from vllm.attention.layer import Attention
 from vllm.config import VllmConfig
@@ -73,6 +74,10 @@ class TPUModelRunner:
         scheduler_config = self.scheduler_config
         parallel_config = self.parallel_config
         self.device = device
+        self.check_recompilation = envs.VLLM_XLA_CHECK_RECOMPILATION
+        if self.check_recompilation:
+            self.num_xla_graphs = xr.get_num_cached_compilation_graph()
+        self.enforce_eager = model_config.enforce_eager
         self.pin_memory = is_pin_memory_available()
         self.dtype = self.model_config.dtype
 
@@ -671,6 +676,12 @@ class TPUModelRunner:
             logprobs=None,
             prompt_logprobs_dict=prompt_logprobs_dict,
         )
+        # Check there is no new graph compilation, all the graphs should be
+        # captured and compiled during warming up.
+        if self.check_recompilation and not self.enforce_eager:
+            curr_cached_graph = xr.get_num_cached_compilation_graph()
+            assert self.num_xla_graphs == curr_cached_graph, (
+                "Recompilation after warm up is detected.")
         return model_runner_output
 
     def load_model(self) -> None:
@@ -810,6 +821,14 @@ class TPUModelRunner:
         xm.wait_device_ops()
         end = time.perf_counter()
         logger.info("Compilation finished in in %.2f [secs].", end - start)
+        # Record the number cached XLA graph after warming up, this will be
+        # used for checking there is no additional graph compilation during
+        # runtime execution.
+        if self.check_recompilation:
+            total_cached_graphs = xr.get_num_cached_compilation_graph()
+            num_compiled_graphs = total_cached_graphs - self.num_xla_graphs
+            logger.info("Compiled %d XLA graphs.", num_compiled_graphs)
+            self.num_xla_graphs += num_compiled_graphs
 
     def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
         """