diff --git a/vllm/model_executor/models/qwen3_vl.py b/vllm/model_executor/models/qwen3_vl.py
index fa6b71bf92682..7f0c9372991d1 100644
--- a/vllm/model_executor/models/qwen3_vl.py
+++ b/vllm/model_executor/models/qwen3_vl.py
@@ -25,7 +25,7 @@
 """Inference-only Qwen3VL model compatible with HuggingFace weights."""
 
 from collections.abc import Callable, Iterable, Iterator, Mapping, Sequence
-from functools import partial
+from functools import lru_cache, partial
 from itertools import islice
 from typing import Any
 
@@ -416,30 +416,41 @@ class Qwen3_VisionTransformer(nn.Module):
     def device(self) -> torch.device:
         return self.patch_embed.proj.weight.device
 
-    def rot_pos_emb(self, grid_thw: list[list[int]]):
-        pos_ids = []
-        max_grid_size = max(max(h, w) for _, h, w in grid_thw)
-        for t, h, w in grid_thw:
-            hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
-            hpos_ids = hpos_ids.reshape(
-                h // self.spatial_merge_size,
-                self.spatial_merge_size,
-                w // self.spatial_merge_size,
-                self.spatial_merge_size,
-            )
-            hpos_ids = hpos_ids.permute(0, 2, 1, 3)
-            hpos_ids = hpos_ids.flatten()
+    @staticmethod
+    @lru_cache(maxsize=1024)
+    def rot_pos_ids(h: int, w: int, spatial_merge_size: int) -> torch.Tensor:
+        hpos_ids = np.broadcast_to(np.arange(h).reshape(h, 1), (h, w))
+        h_div = h // spatial_merge_size
+        w_div = w // spatial_merge_size
+        hpos_ids = hpos_ids.reshape(
+            h_div,
+            spatial_merge_size,
+            w_div,
+            spatial_merge_size,
+        )
+        hpos_ids = hpos_ids.transpose(0, 2, 1, 3)
+        hpos_ids = hpos_ids.flatten()
 
-            wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
-            wpos_ids = wpos_ids.reshape(
-                h // self.spatial_merge_size,
-                self.spatial_merge_size,
-                w // self.spatial_merge_size,
-                self.spatial_merge_size,
-            )
-            wpos_ids = wpos_ids.permute(0, 2, 1, 3)
-            wpos_ids = wpos_ids.flatten()
-            pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
+        wpos_ids = np.broadcast_to(np.arange(w).reshape(1, w), (h, w))
+        wpos_ids = wpos_ids.reshape(
+            h_div,
+            spatial_merge_size,
+            w_div,
+            spatial_merge_size,
+        )
+        wpos_ids = wpos_ids.transpose(0, 2, 1, 3)
+        wpos_ids = wpos_ids.flatten()
+
+        return torch.from_numpy(np.stack([hpos_ids, wpos_ids], axis=-1))
+
+    def rot_pos_emb(self, grid_thw: list[list[int]]):
+        max_grid_size = max(max(h, w) for _, h, w in grid_thw)
+        pos_ids = [
+            self.rot_pos_ids(h, w, self.spatial_merge_size)
+            if t == 1
+            else self.rot_pos_ids(h, w, self.spatial_merge_size).repeat(t, 1)
+            for t, h, w in grid_thw
+        ]
         pos_ids = torch.cat(pos_ids, dim=0)
         rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
         rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)