diff --git a/python/sglang/srt/model_executor/cuda_graph_runner.py b/python/sglang/srt/model_executor/cuda_graph_runner.py
index 303919505..537dab9eb 100644
--- a/python/sglang/srt/model_executor/cuda_graph_runner.py
+++ b/python/sglang/srt/model_executor/cuda_graph_runner.py
@@ -33,7 +33,11 @@ from sglang.srt.distributed.device_communicators.pynccl_allocator import (
     set_graph_pool_id,
 )
 from sglang.srt.distributed.parallel_state import GroupCoordinator, graph_capture
-from sglang.srt.layers.dp_attention import DPPaddingMode, get_attention_tp_size
+from sglang.srt.layers.dp_attention import (
+    DPPaddingMode,
+    get_attention_tp_rank,
+    get_attention_tp_size,
+)
 from sglang.srt.layers.logits_processor import LogitsProcessorOutput
 from sglang.srt.layers.torchao_utils import save_gemlite_cache
 from sglang.srt.model_executor.forward_batch_info import (
@@ -255,6 +259,9 @@ class CudaGraphRunner:
         self.dp_size = model_runner.server_args.dp_size
         self.pp_size = model_runner.server_args.pp_size
 
+        self.attn_tp_size = get_attention_tp_size()
+        self.attn_tp_rank = get_attention_tp_rank()
+
         # Batch sizes to capture
         self.capture_bs, self.compile_bs = get_batch_sizes_to_capture(model_runner)
         rank0_log(f"Capture cuda graph bs {self.capture_bs}")
@@ -749,7 +756,17 @@ class CudaGraphRunner:
             self.global_num_tokens_gpu.fill_(bs * self.num_tokens_per_bs)
             self.global_num_tokens_for_logprob_gpu.fill_(bs * self.num_tokens_per_bs)
         if enable_num_token_non_padded(self.model_runner.server_args):
-            self.num_token_non_padded.copy_(forward_batch.num_token_non_padded)
+            num_token_non_padded = forward_batch.num_token_non_padded
+            if self.require_gathered_buffer:
+                tokens_per_rank = bs // self.attn_tp_size * self.num_tokens_per_bs
+                num_local_token_non_padded = torch.clamp(
+                    num_token_non_padded - tokens_per_rank * self.attn_tp_rank,
+                    min=0,
+                    max=tokens_per_rank,
+                )
+                self.num_token_non_padded.copy_(num_local_token_non_padded)
+            else:
+                self.num_token_non_padded.copy_(num_token_non_padded)
         if self.enable_two_batch_overlap:
             self.tbo_plugin.replay_prepare(
                 forward_mode=self.capture_forward_mode,