diff --git a/vllm/model_executor/models/qwen3_vl.py b/vllm/model_executor/models/qwen3_vl.py
index 6a7d2eaeab3b..9f7a77338452 100644
--- a/vllm/model_executor/models/qwen3_vl.py
+++ b/vllm/model_executor/models/qwen3_vl.py
@@ -467,8 +467,6 @@ def fast_pos_embed_interpolate(self, grid_thw: list[list[int]]) -> torch.Tensor:
             dh_grid, dw_grid = torch.meshgrid(dh, dw, indexing="ij")
             h_floor_grid, w_floor_grid = torch.meshgrid(h_floor, w_floor, indexing="ij")
             h_ceil_grid, w_ceil_grid = torch.meshgrid(h_ceil, w_ceil, indexing="ij")
-            h_floor_grid_idx = h_floor_grid * num_grid_per_side
-            h_ceil_grid_idx = h_ceil_grid * num_grid_per_side
 
             # original computation of weights
             # w00 = (1 - dh_grid) * (1 - dw_grid)
@@ -480,30 +478,25 @@ def fast_pos_embed_interpolate(self, grid_thw: list[list[int]]) -> torch.Tensor:
             w11 = dh_grid * dw_grid
             w10 = dh_grid - w11
             w01 = dw_grid - w11
-            w00 = 1 - dh_grid - dw_grid + w11
+            w00 = 1 - dh_grid - w01
 
-            idx00 = h_floor_grid_idx + w_floor_grid
-            idx01 = h_floor_grid_idx + w_ceil_grid
-            idx10 = h_ceil_grid_idx + w_floor_grid
-            idx11 = h_ceil_grid_idx + w_ceil_grid
+            h_grid = torch.stack([h_floor_grid, h_floor_grid, h_ceil_grid, h_ceil_grid])
+            w_grid = torch.stack([w_floor_grid, w_ceil_grid, w_floor_grid, w_ceil_grid])
+            h_grid_idx = h_grid * num_grid_per_side
 
-            indices = torch.stack([idx00, idx01, idx10, idx11], dim=0).reshape(4, -1)
+            indices = (h_grid_idx + w_grid).reshape(4, -1)
             weights = torch.stack([w00, w01, w10, w11], dim=0).reshape(4, -1, 1)
-            weights = weights.to(
-                dtype=self.dtype, device=self.device, non_blocking=True
-            )
+            weights = weights.to(dtype=self.dtype)
 
             embeds = self.pos_embed(indices)
             weighted_embeds = embeds * weights
-            p0, p1, p2, p3 = weighted_embeds.unbind(dim=0)
-            combined = p0 + p1 + p2 + p3
+            combined = weighted_embeds.sum(dim=0)
 
-            combined = combined.view(h * w, hidden_dim)
-            repeated = combined.unsqueeze(0).expand(t, -1, -1).contiguous()
-            repeated = repeated.view(
-                t, h // m_size, m_size, w // m_size, m_size, hidden_dim
+            combined = combined.reshape(
+                h // m_size, m_size, w // m_size, m_size, hidden_dim
             )
-            repeated = repeated.permute(0, 1, 3, 2, 4, 5).reshape(-1, hidden_dim)
+            combined = combined.permute(0, 2, 1, 3, 4).reshape(1, -1, hidden_dim)
+            repeated = combined.expand(t, -1, -1).reshape(-1, hidden_dim)
             outputs.append(repeated)
 
         return torch.cat(outputs, dim=0)