diff --git a/tensorrt_llm/_torch/speculative/interface.py b/tensorrt_llm/_torch/speculative/interface.py
index db2a5621db..998f4b28cb 100644
--- a/tensorrt_llm/_torch/speculative/interface.py
+++ b/tensorrt_llm/_torch/speculative/interface.py
@@ -110,13 +110,18 @@ class SpeculativeDecodingMode(IntEnum):
     def extend_ctx(self, attention_backend: Type[AttentionBackend]):
         """
         If true, treat generation requests with draft tokens as
-        chunked context requests at the kernel level. Required for
-        any spec dec mode that uses the SpecExecutor.
+        chunked context requests at the kernel level.
         """
 
         if self.use_one_engine():
             # 1-model has separate logic for handling draft tokens
             return False
+
+        if issubclass(attention_backend,
+                      TrtllmAttention) and self.is_mtp_eagle():
+            # TRTLLM MLA does not work with the chunked context mode.
+            return False
+
         return not issubclass(attention_backend,
                               TrtllmAttention) or get_sm_version() != 100
 
diff --git a/tensorrt_llm/_torch/speculative/model_drafter.py b/tensorrt_llm/_torch/speculative/model_drafter.py
index 0a1a58d857..78c0938a99 100644
--- a/tensorrt_llm/_torch/speculative/model_drafter.py
+++ b/tensorrt_llm/_torch/speculative/model_drafter.py
@@ -165,6 +165,9 @@ class ModelDrafter(Drafter):
         input_tokens = get_draft_model_prompt(self.spec_config.spec_dec_mode,
                                               request.get_tokens(0))
 
+        is_eagle_style = self.spec_config.spec_dec_mode.is_eagle3(
+        ) or self.spec_config.spec_dec_mode.is_mtp_eagle()
+
         # First time seeing this request - context request
         if request.max_beam_num_tokens - 1 == request.py_prompt_len:
             # This is the first time the draft model is seeing this request.
@@ -174,10 +177,8 @@ class ModelDrafter(Drafter):
             return self._create_context_request(request, input_tokens)
 
         # For TRTLLM attention backend, we need to create a generation request for both no tokens accepted and tokens accepted
-        elif issubclass(
-                self.draft_model_engine.attn_backend, TrtllmAttention
-        ) and self.use_static_draft_loop and self.spec_config.spec_dec_mode.is_eagle3(
-        ):
+        elif issubclass(self.draft_model_engine.attn_backend, TrtllmAttention
+                        ) and self.use_static_draft_loop and is_eagle_style:
             return self._create_accepted_tokens_request_for_trtllm_attn(
                 request, input_tokens, num_accepted_tokens)
 
diff --git a/tests/integration/defs/test_e2e.py b/tests/integration/defs/test_e2e.py
index 0cfe1449ac..d2d45b0c85 100644
--- a/tests/integration/defs/test_e2e.py
+++ b/tests/integration/defs/test_e2e.py
@@ -1953,18 +1953,17 @@ def test_ptp_quickstart_advanced_mtp_eagle(llm_root, llm_venv, model_name,
                                      dir="./",
                                      delete=True,
                                      delete_on_close=True) as running_log:
-        llm_venv.run_cmd(
-            [
-                str(example_root / "quickstart_advanced.py"),
-                "--use_cuda_graph",
-                "--spec_decode_max_draft_len",
-                "1",  # test 1 MTP module
-                "--spec_decode_algo",
-                "MTP",
-                "--model_dir",
-                f"{llm_models_root()}/{model_path}",
-            ],
-            stdout=running_log)
+        llm_venv.run_cmd([
+            str(example_root / "quickstart_advanced.py"),
+            "--use_cuda_graph",
+            "--spec_decode_max_draft_len",
+            "3",
+            "--spec_decode_algo",
+            "MTP",
+            "--model_dir",
+            f"{llm_models_root()}/{model_path}",
+        ],
+                         stdout=running_log)
         # 74.60 is the memory usage for DeepSeek-V3-Lite-BF16 with MTP Eagle 2 two model style as one extra kv cache is needed for draft model.
         _check_mem_usage(running_log, [74.60, 0, 0, 0])