diff --git a/tests/distributed/test_pipeline_parallel.py b/tests/distributed/test_pipeline_parallel.py
index 7f555ed9168a4..d666b8a1d44bd 100644
--- a/tests/distributed/test_pipeline_parallel.py
+++ b/tests/distributed/test_pipeline_parallel.py
@@ -61,3 +61,27 @@ def test_compare_tp(TP_SIZE, PP_SIZE, EAGER_MODE, CHUNKED_PREFILL, MODEL_NAME,
         tp_args.append("--enforce-eager")
 
     compare_two_settings(MODEL_NAME, pp_args, tp_args)
+
+
+@pytest.mark.parametrize("PP_SIZE, MODEL_NAME", [
+    (2, "JackFram/llama-160m"),
+])
+@pytest.mark.parametrize("ATTN_BACKEND", [
+    "FLASH_ATTN",
+    "FLASHINFER",
+])
+def test_pp_cudagraph(PP_SIZE, MODEL_NAME, ATTN_BACKEND):
+    cudagraph_args = [
+        # use half precision for speed and memory savings in CI environment
+        "--dtype",
+        "float16",
+        "--pipeline-parallel-size",
+        str(PP_SIZE),
+        "--distributed-executor-backend",
+        "ray",
+    ]
+    os.environ["VLLM_ATTENTION_BACKEND"] = ATTN_BACKEND
+
+    eager_args = cudagraph_args + ["--enforce-eager"]
+
+    compare_two_settings(MODEL_NAME, eager_args, cudagraph_args)
diff --git a/vllm/worker/model_runner.py b/vllm/worker/model_runner.py
index e63be184af16a..073c5a73f739b 100644
--- a/vllm/worker/model_runner.py
+++ b/vllm/worker/model_runner.py
@@ -1040,9 +1040,9 @@ def capture_model(self, kv_caches: List[List[torch.Tensor]]) -> None:
                     self.parallel_config.pipeline_parallel_size):
                 for batch_size in reversed(batch_size_capture_list):
                     if self.attn_backend.get_name() == "flashinfer":
-                        indptr_buffer = indptr_buffer[:batch_size + 1]
-                        last_page_len_buffer = last_page_len_buffer[:
-                                                                    batch_size]
+                        _indptr_buffer = indptr_buffer[:batch_size + 1]
+                        _last_page_len_buffer = last_page_len_buffer[:
+                                                                     batch_size]
 
                         num_qo_heads = (
                             self.model_config.get_num_attention_heads(
@@ -1055,8 +1055,8 @@ def capture_model(self, kv_caches: List[List[torch.Tensor]]) -> None:
                             use_tensor_cores = False
                         decode_wrapper = \
                             CUDAGraphBatchDecodeWithPagedKVCacheWrapper(
-                            decode_workspace_buffer, indptr_buffer,
-                            indices_buffer, last_page_len_buffer, "NHD",
+                            decode_workspace_buffer, _indptr_buffer,
+                            indices_buffer, _last_page_len_buffer, "NHD",
                             use_tensor_cores)
                         kv_cache_dtype = get_kv_cache_torch_dtype(
                             self.kv_cache_dtype, self.model_config.dtype)
@@ -1131,10 +1131,10 @@ def capture_model(self, kv_caches: List[List[torch.Tensor]]) -> None:
                         self.model, self.attn_backend.get_name())
 
                     if self.attn_backend.get_name() == "flashinfer":
-                        graph_runner.flashinfer_indptr_buffer = indptr_buffer
+                        graph_runner.flashinfer_indptr_buffer = _indptr_buffer
                         graph_runner.flashinfer_indices_buffer = indices_buffer
                         graph_runner.flashinfer_last_page_len_buffer = \
-                            last_page_len_buffer
+                            _last_page_len_buffer
                         graph_runner.flashinfer_decode_workspace_buffer = \
                                 decode_workspace_buffer
                         graph_runner.flashinfer_decode_wrapper = \