diff --git a/vllm/worker/model_runner.py b/vllm/worker/model_runner.py
index bbadc0e0..460d9907 100644
--- a/vllm/worker/model_runner.py
+++ b/vllm/worker/model_runner.py
@@ -506,7 +506,9 @@ class ModelRunner:
                     "use '--enforce-eager' in the CLI.")
         logger.info("CUDA graphs can take additional 1~3 GiB memory per GPU. "
                     "If you are running out of memory, consider decreasing "
-                    "`gpu_memory_utilization` or enforcing eager mode.")
+                    "`gpu_memory_utilization` or enforcing eager mode. "
+                    "You can also reduce the `max_num_seqs` as needed "
+                    "to decrease memory usage.")
         start_time = time.perf_counter()
 
         # Prepare dummy inputs. These will be reused for all batch sizes.
@@ -519,9 +521,15 @@ class ModelRunner:
         context_lens = torch.ones(max_batch_size, dtype=torch.int32).cuda()
         block_tables = torch.from_numpy(self.graph_block_tables).cuda()
 
+        graph_batch_size = _get_graph_batch_size(
+            self.scheduler_config.max_num_seqs)
+        batch_size_capture_list = [
+            bs for bs in _BATCH_SIZES_TO_CAPTURE if bs <= graph_batch_size
+        ]
+
         # NOTE: Capturing the largest batch size first may help reduce the
         # memory usage of CUDA graph.
-        for batch_size in reversed(_BATCH_SIZES_TO_CAPTURE):
+        for batch_size in reversed(batch_size_capture_list):
             # Create dummy input_metadata.
             input_metadata = InputMetadata(
                 is_prompt=False,