diff --git a/tensorrt_llm/_torch/pyexecutor/cuda_graph_runner.py b/tensorrt_llm/_torch/pyexecutor/cuda_graph_runner.py
index 187566f62eb..852f2e063da 100644
--- a/tensorrt_llm/_torch/pyexecutor/cuda_graph_runner.py
+++ b/tensorrt_llm/_torch/pyexecutor/cuda_graph_runner.py
@@ -18,6 +18,7 @@
 from ..speculative.mtp import SampleStateTensorsMTP
 from ..utils import make_weak_ref, piecewise_cuda_graph
 from .llm_request import get_draft_token_length
+from .mamba_cache_manager import MambaCacheManager
 from .resource_manager import (BaseResourceManager, ResourceManager,
                                ResourceManagerType)
 from .sampler import SampleStateTensors
@@ -450,6 +451,11 @@ def _get_padded_batch(self, batch: ScheduledRequests,
             if spec_res_mgr:
                 spec_res_mgr.add_dummy_requests([CUDA_GRAPH_DUMMY_REQUEST_ID])
 
+        # handle special cases of padding requests + MambaCacheManager or MambaHybridCacheManager
+        if isinstance(kv_cache_manager, MambaCacheManager):
+            kv_cache_manager.reorder_state_indices_when_padding_requests(
+                batch_size, padding_size)
+
         self.padding_dummy_request.py_draft_tokens = [0] * runtime_draft_len
         batch.generation_requests.extend([self.padding_dummy_request] *
                                          padding_size)
diff --git a/tensorrt_llm/_torch/pyexecutor/mamba_cache_manager.py b/tensorrt_llm/_torch/pyexecutor/mamba_cache_manager.py
index 767a70dd416..f4af1975a1c 100644
--- a/tensorrt_llm/_torch/pyexecutor/mamba_cache_manager.py
+++ b/tensorrt_llm/_torch/pyexecutor/mamba_cache_manager.py
@@ -127,6 +127,23 @@ def _prepare_mamba_cache_blocks(self, request_ids: List[int]):
             state_indices, dtype=torch.int32, pin_memory=True),
                                                       non_blocking=True)
 
+    # When there exists padded requests, the state indices should not be repeated.
+    def reorder_state_indices_when_padding_requests(self, request_size,
+                                                    padding_size):
+        if padding_size == 0:
+            return
+
+        # we can use mamba_cache_free_blocks for padding_requests
+        assert len(
+            self.mamba_cache_free_blocks
+        ) >= padding_size, "Padding requests run out of available mamba cache blocks"
+        self.state_indices[request_size:request_size +
+                           padding_size] = torch.tensor(
+                               self.mamba_cache_free_blocks[:padding_size],
+                               dtype=self.state_indices.dtype,
+                               pin_memory=True).to(self.state_indices.device,
+                                                   non_blocking=True)
+
     def prepare_resources(self, scheduled_batch: ScheduledRequests):
         context_ids = [
             i.py_request_id for i in scheduled_batch.context_requests
diff --git a/tests/integration/defs/accuracy/test_llm_api_pytorch.py b/tests/integration/defs/accuracy/test_llm_api_pytorch.py
index 76f45b15137..909d18ceac4 100644
--- a/tests/integration/defs/accuracy/test_llm_api_pytorch.py
+++ b/tests/integration/defs/accuracy/test_llm_api_pytorch.py
@@ -4602,10 +4602,12 @@ def test_bf16_4gpu(self, tp_size, pp_size, ep_size, cuda_graph,
         model_path = f"{self.MODEL_PATH}/Qwen3-Next-80B-A3B-Instruct"
         kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.6,
                                         enable_block_reuse=False)
-        pytorch_config = dict(disable_overlap_scheduler=not overlap_scheduler,
-                              cuda_graph_config=CudaGraphConfig(
-                                  max_batch_size=512, enable_padding=True)
-                              if cuda_graph else None)
+        pytorch_config = dict(
+            disable_overlap_scheduler=not overlap_scheduler,
+            cuda_graph_config=CudaGraphConfig(
+                enable_padding=True,
+                batch_sizes=[1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048])
+            if cuda_graph else None)
 
         with LLM(
                 model_path,
@@ -4620,6 +4622,7 @@ def test_bf16_4gpu(self, tp_size, pp_size, ep_size, cuda_graph,
             task.evaluate(llm)
             mocker.patch.object(GSM8K, "MAX_OUTPUT_LEN",
                                 self.GSM8K_MAX_OUTPUT_LEN)
+            mocker.patch.object(GSM8K, "NUM_SAMPLES", 1319)
             task = GSM8K(self.MODEL_NAME)
             task.evaluate(llm)