diff --git a/test/srt/test_eagle_infer.py b/test/srt/test_eagle_infer.py
index b04b13211..4a6170320 100644
--- a/test/srt/test_eagle_infer.py
+++ b/test/srt/test_eagle_infer.py
@@ -20,79 +20,78 @@ from sglang.test.test_utils import (
 
 
 class TestEAGLEEngine(unittest.TestCase):
+    BASE_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,
+    }
 
-    def test_eagle_accuracy(self):
-        prompt1 = "Today is a sunny day and I like"
-        sampling_params1 = {"temperature": 0, "max_new_tokens": 8}
+    def setUp(self):
+        self.prompt = "Today is a sunny day and I like"
+        self.sampling_params = {"temperature": 0, "max_new_tokens": 8}
 
-        # Get the reference output
         ref_engine = sgl.Engine(model_path=DEFAULT_EAGLE_TARGET_MODEL_FOR_TEST)
-        ref_output = ref_engine.generate(prompt1, sampling_params1)["text"]
+        self.ref_output = ref_engine.generate(self.prompt, self.sampling_params)["text"]
         ref_engine.shutdown()
 
-        # Test cases with different configurations
+    def test_eagle_accuracy(self):
         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,
-            },
+            self.BASE_CONFIG,
+            {**self.BASE_CONFIG, "disable_cuda_graph": True},
         ]
 
         for config in configs:
-            # Launch EAGLE engine
-            engine = sgl.Engine(**config)
+            with self.subTest(
+                cuda_graph=(
+                    "enabled" if len(config) == len(self.BASE_CONFIG) else "disabled"
+                )
+            ):
+                engine = sgl.Engine(**config)
+                try:
+                    self._test_basic_generation(engine)
+                    self._test_eos_token(engine)
+                    self._test_batch_generation(engine)
+                finally:
+                    engine.shutdown()
 
-            # Case 1: Test the output of EAGLE engine is the same as normal engine
-            out1 = engine.generate(prompt1, sampling_params1)["text"]
-            print(f"{out1=}, {ref_output=}")
-            self.assertEqual(out1, ref_output)
+    def _test_basic_generation(self, engine):
+        output = engine.generate(self.prompt, self.sampling_params)["text"]
+        print(f"{output=}, {self.ref_output=}")
+        self.assertEqual(output, self.ref_output)
 
-            # Case 2: Test the output of EAGLE engine does not contain unexpected EOS
-            prompt2 = "[INST] <<SYS>>\\nYou are a helpful assistant.\\n<</SYS>>\\nToday is a sunny day and I like [/INST]"
-            sampling_params2 = {
-                "temperature": 0,
-                "max_new_tokens": 1024,
-                "skip_special_tokens": False,
-            }
+    def _test_eos_token(self, engine):
+        prompt = "[INST] <<SYS>>\nYou are a helpful assistant.\n<</SYS>>\nToday is a sunny day and I like [/INST]"
+        params = {
+            "temperature": 0,
+            "max_new_tokens": 1024,
+            "skip_special_tokens": False,
+        }
 
-            tokenizer = get_tokenizer(DEFAULT_EAGLE_TARGET_MODEL_FOR_TEST)
-            out2 = engine.generate(prompt2, sampling_params2)["text"]
-            print(f"{out2=}")
-            tokens = tokenizer.encode(out2, truncation=False)
-            assert tokenizer.eos_token_id not in tokens
+        tokenizer = get_tokenizer(DEFAULT_EAGLE_TARGET_MODEL_FOR_TEST)
+        output = engine.generate(prompt, params)["text"]
+        print(f"{output=}")
 
-            # 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_params3 = {"temperature": 0, "max_new_tokens": 30}
-            outputs = engine.generate(prompts, sampling_params3)
-            for prompt, output in zip(prompts, outputs):
-                print("===============================")
-                print(f"Prompt: {prompt}\nGenerated text: {output['text']}")
+        tokens = tokenizer.encode(output, truncation=False)
+        self.assertNotIn(tokenizer.eos_token_id, tokens)
 
-            # Shutdown the engine
-            engine.shutdown()
+    def _test_batch_generation(self, engine):
+        prompts = [
+            "Hello, my name is",
+            "The president of the United States is",
+            "The capital of France is",
+            "The future of AI is",
+        ]
+        params = {"temperature": 0, "max_new_tokens": 30}
+
+        outputs = engine.generate(prompts, params)
+        for prompt, output in zip(prompts, outputs):
+            print(f"Prompt: {prompt}")
+            print(f"Generated: {output['text']}")
+            print("-" * 40)
 
 
 prompts = [