diff --git a/python/sglang/srt/speculative/eagle_worker_v2.py b/python/sglang/srt/speculative/eagle_worker_v2.py
index 01a07ad53..af883a455 100644
--- a/python/sglang/srt/speculative/eagle_worker_v2.py
+++ b/python/sglang/srt/speculative/eagle_worker_v2.py
@@ -732,6 +732,13 @@ class EAGLEWorkerV2(BaseSpecWorker):
                     model_worker_batch
                 )
             assert verify_input.is_verify_input()
+            # Record a CUDA event after draft() GPU work is dispatched.
+            # This event will be waited on by plan_stream in verify()
+            # to ensure draft CUDA graph kernels finish before plan_stream
+            # begins metadata preparation.
+            if self.plan_stream:
+                self._draft_done_event = torch.get_device_module(self.device).Event()
+                self._draft_done_event.record()
             model_worker_batch.spec_info = verify_input
             batch_output = self.verify(model_worker_batch)
             with self.draft_worker.draft_tp_context(
@@ -758,6 +765,12 @@ class EAGLEWorkerV2(BaseSpecWorker):
         # Batch 1: Target verify
         # Prepare for target verify in a separate stream
         with self.plan_stream_ctx:
+            # Wait for the draft CUDA graph to finish before plan_stream
+            # begins its work. Using an event is more targeted than
+            # wait_stream(main_stream) — it only waits for draft GPU
+            # work, not all queued main_stream operations.
+            if self.plan_stream and hasattr(self, "_draft_done_event"):
+                self.plan_stream.wait_event(self._draft_done_event)
             verify_forward_batch, can_run_cuda_graph = (
                 verify_input.prepare_for_v2_verify(
                     self.req_to_token_pool,
diff --git a/python/sglang/srt/speculative/multi_layer_eagle_worker_v2.py b/python/sglang/srt/speculative/multi_layer_eagle_worker_v2.py
index 40eb2ad66..b54dc3363 100644
--- a/python/sglang/srt/speculative/multi_layer_eagle_worker_v2.py
+++ b/python/sglang/srt/speculative/multi_layer_eagle_worker_v2.py
@@ -684,6 +684,10 @@ class MultiLayerEagleWorkerV2(BaseSpecWorker):
             draft_input: EagleDraftInput = model_worker_batch.spec_info
             verify_input: EagleVerifyInput = self.draft_worker.draft(model_worker_batch)
             assert verify_input.is_verify_input()
+            # Record a CUDA event after draft() GPU work is dispatched.
+            if self.plan_stream:
+                self._draft_done_event = torch.get_device_module(self.device).Event()
+                self._draft_done_event.record()
             model_worker_batch.spec_info = verify_input
             batch_output = self.verify(model_worker_batch)
             self.draft_worker._draft_extend_for_decode(model_worker_batch, batch_output)
@@ -707,6 +711,10 @@ class MultiLayerEagleWorkerV2(BaseSpecWorker):
         # Batch 1: Target verify
         # Prepare for target verify in a separate stream
         with self.plan_stream_ctx:
+            # Wait for the draft CUDA graph to finish before plan_stream
+            # begins its work.
+            if self.plan_stream and hasattr(self, "_draft_done_event"):
+                self.plan_stream.wait_event(self._draft_done_event)
             verify_forward_batch, can_run_cuda_graph = (
                 verify_input.prepare_for_v2_verify(
                     self.req_to_token_pool,