diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py
index 4a6856bf4fef..08e13ab887bf 100644
--- a/vllm/v1/worker/gpu_model_runner.py
+++ b/vllm/v1/worker/gpu_model_runner.py
@@ -783,28 +783,8 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
 
         logits_indices_padded = None
         if self.cache_config.kv_sharing_fast_prefill:
-            assert self.kv_sharing_fast_prefill_logits_indices is not None
-            num_logits = logits_indices.shape[0]
-            assert num_logits > 0
-            self.kv_sharing_fast_prefill_logits_indices[:num_logits].copy_(
+            logits_indices_padded = self._prepare_kv_sharing_fast_prefill(
                 logits_indices)
-            # There might have leftover indices in logits_indices[num_logits:]
-            # from previous iterations, whose values may be greater than the
-            # batch size in the current iteration. To ensure indices are always
-            # valid, we fill the padded indices with the last index.
-            self.kv_sharing_fast_prefill_logits_indices[num_logits:].fill_(
-                logits_indices[-1].item())
-            if (self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
-                    and num_logits <= self.cudagraph_batch_sizes[-1]):
-                # Use piecewise CUDA graphs.
-                # Add padding to the batch size.
-                num_logits_padded = self.vllm_config.pad_for_cudagraph(
-                    num_logits)
-            else:
-                num_logits_padded = num_logits
-            logits_indices_padded = (
-                self.kv_sharing_fast_prefill_logits_indices[:num_logits_padded]
-            )
 
         attn_metadata: dict[str, Any] = {}
 
@@ -1109,6 +1089,32 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         )
         return metadata
 
+    def _prepare_kv_sharing_fast_prefill(
+        self,
+        logits_indices: torch.Tensor,
+    ) -> torch.Tensor:
+        assert self.kv_sharing_fast_prefill_logits_indices is not None
+        num_logits = logits_indices.shape[0]
+        assert num_logits > 0
+        self.kv_sharing_fast_prefill_logits_indices[:num_logits].copy_(
+            logits_indices)
+        # There might have leftover indices in logits_indices[num_logits:]
+        # from previous iterations, whose values may be greater than the
+        # batch size in the current iteration. To ensure indices are always
+        # valid, we fill the padded indices with the last index.
+        self.kv_sharing_fast_prefill_logits_indices[num_logits:].fill_(
+            logits_indices[-1].item())
+        if (self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
+                and num_logits <= self.cudagraph_batch_sizes[-1]):
+            # Use piecewise CUDA graphs.
+            # Add padding to the batch size.
+            num_logits_padded = self.vllm_config.pad_for_cudagraph(num_logits)
+        else:
+            num_logits_padded = num_logits
+        logits_indices_padded = (
+            self.kv_sharing_fast_prefill_logits_indices[:num_logits_padded])
+        return logits_indices_padded
+
     def _execute_mm_encoder(self, scheduler_output: "SchedulerOutput"):
         scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
         if not scheduled_encoder_inputs: