diff --git a/tensorrt_llm/_torch/pyexecutor/model_engine.py b/tensorrt_llm/_torch/pyexecutor/model_engine.py
index db38219fe03..ef8bb0aea3b 100644
--- a/tensorrt_llm/_torch/pyexecutor/model_engine.py
+++ b/tensorrt_llm/_torch/pyexecutor/model_engine.py
@@ -695,19 +695,25 @@ def release_batch(result: ScheduledRequests | None):
         cuda_graph_batch_sizes = sorted(self._cuda_graph_batch_sizes,
                                         reverse=True)
         # Create CUDA graphs for different draft lengths
-        draft_lengths = [self.max_draft_len]
-        # For non-draft model, we also capture the CUDA graph instance for draft length 0,
-        # so that when we disable spec decode at runtime, we can still run the captured graph.
-        # Note that for one engine mode, we are not able to turn off spec decode at runtime.
-        if (not self.is_draft_model and self.max_draft_len > 0
-                and not self.spec_config.spec_dec_mode.use_one_engine()
-                # Assume that speculation is always on if the user didn't give us a max_concurrency
-                # value. This will save on memory.
-                and self.spec_config.max_concurrency is not None):
-            draft_lengths.append(0)
-        if self.is_spec_decode and self.is_draft_model and spec_resource_manager is not None and isinstance(
-                spec_resource_manager, Eagle3ResourceManager):
-            draft_lengths.append(self.original_max_draft_len)
+        draft_lengths = []
+        if self.is_draft_model:
+            if self.model_is_wrapped and self.is_spec_decode and spec_resource_manager is not None and isinstance(
+                    spec_resource_manager, Eagle3ResourceManager):
+                # The CDL path uses draft_len > 0 for the number of iterations in the drafting loop.
+                draft_lengths.append(self.original_max_draft_len)
+            else:
+                draft_lengths.append(self.max_draft_len)
+        else:
+            # For non-draft model, we also capture the CUDA graph instance for draft length 0,
+            # so that when we disable spec decode at runtime, we can still run the captured graph.
+            # Note that for one engine mode, we are not able to turn off spec decode at runtime.
+            if (self.max_draft_len > 0
+                    and not self.spec_config.spec_dec_mode.use_one_engine()
+                    # Assume that speculation is always on if the user didn't give us a max_concurrency
+                    # value. This will save on memory.
+                    and self.spec_config.max_concurrency is not None):
+                draft_lengths.append(0)
+            draft_lengths = [self.max_draft_len]
 
         for bs in cuda_graph_batch_sizes:
             if bs > self.batch_size:
@@ -723,6 +729,7 @@ def release_batch(result: ScheduledRequests | None):
                     logger.info(
                         f"Run generation only CUDA graph warmup for batch size={bs}, draft_len={draft_len}"
                     )
+
                     self.enable_spec_decode = draft_len > 0 or self.is_draft_model
 
                     def _update_draft_inference_state(is_first_draft: bool,