diff --git a/vllm/distributed/parallel_state.py b/vllm/distributed/parallel_state.py
index 50dbbf50e9fcf..c53601a22f215 100644
--- a/vllm/distributed/parallel_state.py
+++ b/vllm/distributed/parallel_state.py
@@ -1315,6 +1315,37 @@ def in_the_same_node_as(pg: Union[ProcessGroup, StatelessProcessGroup],
     return [x == 1 for x in aggregated_data.tolist()]
 
 
+def is_global_first_rank() -> bool:
+    """
+    Check if the current process is the first rank globally across all 
+    parallelism strategies (PP, TP, DP, EP, etc.).
+    
+    Unlike group-specific checks like `get_tensor_model_parallel_rank() == 0`
+    or `get_pp_group().is_first_rank`, this function checks the global rank
+    across all parallelism dimensions.
+    
+    Returns:
+        bool: True if this is the global first rank (rank 0), False otherwise.
+              Returns True if distributed is not initialized (single process).
+    """
+    try:
+        # If world group is available, use it for the most accurate check
+        global _WORLD
+        if _WORLD is not None:
+            return _WORLD.is_first_rank
+
+        # If torch distributed is not initialized, assume single process
+        if not torch.distributed.is_initialized():
+            return True
+
+        # Fallback to torch's global rank
+        return torch.distributed.get_rank() == 0
+
+    except Exception:
+        # If anything goes wrong, assume this is the first rank
+        return True
+
+
 def _node_count(pg: Union[ProcessGroup, StatelessProcessGroup]) -> int:
     """
     Returns the total number of nodes in the process group.
diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py
index 29d39de212f88..5bdaf4b969e70 100644
--- a/vllm/v1/worker/gpu_model_runner.py
+++ b/vllm/v1/worker/gpu_model_runner.py
@@ -26,7 +26,7 @@ from vllm.distributed.kv_transfer import (get_kv_transfer_group,
                                           has_kv_transfer_group)
 from vllm.distributed.kv_transfer.kv_connector.v1 import KVConnectorBase_V1
 from vllm.distributed.parallel_state import (
-    get_pp_group, get_tp_group, graph_capture,
+    get_pp_group, get_tp_group, graph_capture, is_global_first_rank,
     prepare_communication_buffer_for_model)
 from vllm.forward_context import (DPMetadata, get_forward_context,
                                   set_forward_context)
@@ -2285,9 +2285,12 @@ class GPUModelRunner(LoRAModelRunnerMixin):
         # can reuse the memory pool allocated for the large shapes.
         with graph_capture(device=self.device):
             full_cg = self.full_cuda_graph
-            for num_tokens in tqdm(reversed(self.cudagraph_batch_sizes),
-                                   desc="Capturing CUDA graphs",
-                                   total=len(self.cudagraph_batch_sizes)):
+            # Only rank 0 should print progress bar during capture
+            compilation_cases = reversed(self.cudagraph_batch_sizes)
+            if is_global_first_rank():
+                compilation_cases = tqdm(list(compilation_cases),
+                                         desc="Capturing CUDA graph shapes")
+            for num_tokens in compilation_cases:
                 # We skip EPLB here since we don't want to record dummy metrics
                 for _ in range(
                         self.compilation_config.cudagraph_num_of_warmups):