diff --git a/vllm/v1/worker/gpu_model_runner.py b/vllm/v1/worker/gpu_model_runner.py
index 12b7ce18fbc2..d3995b619d31 100644
--- a/vllm/v1/worker/gpu_model_runner.py
+++ b/vllm/v1/worker/gpu_model_runner.py
@@ -2,7 +2,7 @@
 
 import gc
 import time
-from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, cast
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union, cast
 
 import numpy as np
 import torch
@@ -149,6 +149,7 @@ def __init__(
         self.positions = torch.zeros(self.max_num_tokens,
                                      dtype=torch.int64,
                                      device=self.device)
+        # self.intermediate_tensors  # Set after load_model
 
         # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
         if self.uses_mrope:
@@ -869,7 +870,7 @@ def execute_model(
         self,
         scheduler_output: "SchedulerOutput",
         intermediate_tensors: Optional[IntermediateTensors] = None,
-    ) -> ModelRunnerOutput:
+    ) -> Union[ModelRunnerOutput, torch.Tensor]:
         batch_changed = self._update_states(scheduler_output)
 
         if self.is_multimodal_model:
@@ -919,6 +920,14 @@ def execute_model(
         else:
             positions = self.positions[:num_input_tokens]
 
+        if get_pp_group().is_first_rank:
+            intermediate_tensors = None
+        else:
+            intermediate_tensors = IntermediateTensors({
+                k: v[:num_input_tokens]
+                for k, v in self.intermediate_tensors.items()
+            })
+
         # Run the decoder.
         # Use persistent buffers for CUDA graphs.
         with set_forward_context(attn_metadata, self.vllm_config):
@@ -931,7 +940,9 @@ def execute_model(
                 inputs_embeds=inputs_embeds,
             )
         if not get_pp_group().is_last_rank:
+            # For mid-pipeline stages, return the hidden states.
             return hidden_states
+
         hidden_states = hidden_states[:num_scheduled_tokens]
         sample_hidden_states = hidden_states[logits_indices]
         logits = self.model.compute_logits(sample_hidden_states, None)
@@ -1118,12 +1129,21 @@ def _dummy_run(
             positions = self.mrope_positions[:, :num_tokens]
         else:
             positions = self.positions[:num_tokens]
-        intermediate_tensors = None
-        if not get_pp_group().is_first_rank:
-            intermediate_tensors = self.model.make_empty_intermediate_tensors(
-                batch_size=num_tokens,
-                dtype=self.model_config.dtype,
-                device=self.device)
+
+        if get_pp_group().is_first_rank:
+            intermediate_tensors = None
+        else:
+            if not hasattr(self, "intermediate_tensors"):
+                self.intermediate_tensors = (
+                    self.model.make_empty_intermediate_tensors(
+                        batch_size=self.max_num_tokens,
+                        dtype=self.model_config.dtype,
+                        device=self.device))
+            intermediate_tensors = IntermediateTensors({
+                k: v[:num_tokens]
+                for k, v in self.intermediate_tensors.items()
+            })
+
         with set_forward_context(None, self.vllm_config):
             hidden_states = model(
                 input_ids=input_ids,