diff --git a/test/srt/test_eagle_infer.py b/test/srt/test_eagle_infer.py
index b01c26049..b5b17dad1 100644
--- a/test/srt/test_eagle_infer.py
+++ b/test/srt/test_eagle_infer.py
@@ -30,51 +30,69 @@ class TestEAGLEEngine(unittest.TestCase):
         ref_output = ref_engine.generate(prompt, sampling_params)["text"]
         ref_engine.shutdown()
 
-        # Launch EAGLE engine
-        engine = sgl.Engine(
-            model_path=DEFAULT_EAGLE_TARGET_MODEL_FOR_TEST,
-            speculative_draft_model_path=DEFAULT_EAGLE_DRAFT_MODEL_FOR_TEST,
-            speculative_algorithm="EAGLE",
-            speculative_num_steps=5,
-            speculative_eagle_topk=8,
-            speculative_num_draft_tokens=64,
-            mem_fraction_static=0.7,
-        )
-
-        # Case 1: Test the output of EAGLE engine is the same as normal engine
-        out1 = engine.generate(prompt, sampling_params)["text"]
-        print(f"{out1=}, {ref_output=}")
-        self.assertEqual(out1, ref_output)
-
-        # Case 2: Test the output of EAGLE engine does not contain unexpected EOS
-        prompt = "[INST] <<SYS>>\\nYou are a helpful assistant.\\n<</SYS>>\\nToday is a sunny day and I like [/INST]"
-        sampling_params = {
-            "temperature": 0,
-            "max_new_tokens": 1024,
-            "skip_special_tokens": False,
-        }
-
-        tokenizer = get_tokenizer(DEFAULT_EAGLE_TARGET_MODEL_FOR_TEST)
-        out2 = engine.generate(prompt, sampling_params)["text"]
-        print(f"{out2=}")
-        tokens = tokenizer.encode(out2, truncation=False)
-        assert tokenizer.eos_token_id not in tokens
-
-        # Case 3: Batched prompts
-        prompts = [
-            "Hello, my name is",
-            "The president of the United States is",
-            "The capital of France is",
-            "The future of AI is",
+        # Test cases with different configurations
+        configs = [
+            # Original config
+            {
+                "model_path": DEFAULT_EAGLE_TARGET_MODEL_FOR_TEST,
+                "speculative_draft_model_path": DEFAULT_EAGLE_DRAFT_MODEL_FOR_TEST,
+                "speculative_algorithm": "EAGLE",
+                "speculative_num_steps": 5,
+                "speculative_eagle_topk": 8,
+                "speculative_num_draft_tokens": 64,
+                "mem_fraction_static": 0.7,
+            },
+            # Config with CUDA graph disabled
+            {
+                "model_path": DEFAULT_EAGLE_TARGET_MODEL_FOR_TEST,
+                "speculative_draft_model_path": DEFAULT_EAGLE_DRAFT_MODEL_FOR_TEST,
+                "speculative_algorithm": "EAGLE",
+                "speculative_num_steps": 5,
+                "speculative_eagle_topk": 8,
+                "speculative_num_draft_tokens": 64,
+                "mem_fraction_static": 0.7,
+                "disable_cuda_graph": True,
+            },
         ]
-        sampling_params = {"temperature": 0, "max_new_tokens": 30}
-        outputs = engine.generate(prompts, sampling_params)
-        for prompt, output in zip(prompts, outputs):
-            print("===============================")
-            print(f"Prompt: {prompt}\nGenerated text: {output['text']}")
 
-        # Shutdown the engine
-        engine.shutdown()
+        for config in configs:
+            # Launch EAGLE engine
+            engine = sgl.Engine(**config)
+
+            # Case 1: Test the output of EAGLE engine is the same as normal engine
+            out1 = engine.generate(prompt, sampling_params)["text"]
+            print(f"{out1=}, {ref_output=}")
+            self.assertEqual(out1, ref_output)
+
+            # Case 2: Test the output of EAGLE engine does not contain unexpected EOS
+            prompt = "[INST] <<SYS>>\\nYou are a helpful assistant.\\n<</SYS>>\\nToday is a sunny day and I like [/INST]"
+            sampling_params = {
+                "temperature": 0,
+                "max_new_tokens": 1024,
+                "skip_special_tokens": False,
+            }
+
+            tokenizer = get_tokenizer(DEFAULT_EAGLE_TARGET_MODEL_FOR_TEST)
+            out2 = engine.generate(prompt, sampling_params)["text"]
+            print(f"{out2=}")
+            tokens = tokenizer.encode(out2, truncation=False)
+            assert tokenizer.eos_token_id not in tokens
+
+            # Case 3: Batched prompts
+            prompts = [
+                "Hello, my name is",
+                "The president of the United States is",
+                "The capital of France is",
+                "The future of AI is",
+            ]
+            sampling_params = {"temperature": 0, "max_new_tokens": 30}
+            outputs = engine.generate(prompts, sampling_params)
+            for prompt, output in zip(prompts, outputs):
+                print("===============================")
+                print(f"Prompt: {prompt}\nGenerated text: {output['text']}")
+
+            # Shutdown the engine
+            engine.shutdown()
 
 
 prompts = [