diff --git a/tests/integration/defs/accuracy/test_llm_api_pytorch.py b/tests/integration/defs/accuracy/test_llm_api_pytorch.py
index c6699b8bc5ab..862777d4f63e 100644
--- a/tests/integration/defs/accuracy/test_llm_api_pytorch.py
+++ b/tests/integration/defs/accuracy/test_llm_api_pytorch.py
@@ -2093,7 +2093,45 @@ def test_nvfp4_multi_gpus_chunked_prefill(self, tp_size, pp_size, ep_size,
             assert llm.args.moe_config.backend == moe_backend
             assert llm.args.quant_config.quant_algo == QuantAlgo.NVFP4
 
-    @skip_pre_blackwell
+            task = GSM8K(self.MODEL_NAME)
+            task.evaluate(llm)
+
+    def test_nvfp4_multi_gpus_corner_case(self):
+        """
+        This test is used to test the corner case of the NVFP4 model.
+        When using the same value for max_seq_len and max_num_tokens, there will be no
+        enough kv block for the dummy requests in CUDA graph warmup when creating
+        the py_executor before estimating kv cache. Then CUDA graph capture will be
+        triggered when estimating kv cache. This may cause some errors.
+        More info in https://nvbugs/5485325.
+        """
+        kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.80,
+                                        dtype="fp8",
+                                        enable_block_reuse=False)
+        pytorch_config = dict(disable_overlap_scheduler=False,
+                              cuda_graph_config=CudaGraphConfig(
+                                  enable_padding=True, max_batch_size=1024),
+                              moe_config=MoeConfig(backend="TRTLLM"))
+
+        mtp_config = MTPDecodingConfig(num_nextn_predict_layers=1)
+        with LLM(f"{llm_models_root()}/DeepSeek-R1/DeepSeek-R1-FP4",
+                 tensor_parallel_size=8,
+                 pipeline_parallel_size=1,
+                 moe_expert_parallel_size=8,
+                 kv_cache_config=kv_cache_config,
+                 **pytorch_config,
+                 enable_attention_dp=False,
+                 speculative_config=mtp_config,
+                 max_seq_len=5120,
+                 max_num_tokens=5120) as llm:
+
+            assert llm.args.quant_config.quant_algo == QuantAlgo.NVFP4
+
+            task = MMLU(self.MODEL_NAME)
+            task.evaluate(llm)
+            task = GSM8K(self.MODEL_NAME)
+            task.evaluate(llm)
+
     def test_nvfp4_multi_gpus_corner_case(self):
         """
         This test is used to test the corner case of the NVFP4 model.
diff --git a/tests/integration/defs/test_e2e.py b/tests/integration/defs/test_e2e.py
index 4ab3e69c8955..df54cd3e4568 100644
--- a/tests/integration/defs/test_e2e.py
+++ b/tests/integration/defs/test_e2e.py
@@ -2303,6 +2303,7 @@ def test_ptp_quickstart_advanced_8gpus(llm_root, llm_venv, model_name,
             f"{llm_models_root()}/{model_path}",
             "--tp_size=8",
             "--max_batch_size=32",
+            "--max_num_tokens=256",
         ],
                          stdout=running_log)
         if model_name in mapping:
@@ -2366,6 +2367,7 @@ def test_ptp_quickstart_advanced_2gpus_sm120(llm_root, llm_venv, model_name,
         "--model_dir",
         f"{llm_models_root()}/{model_path}",
         "--tp_size=2",
+        "--max_num_tokens=256",
     ])