diff --git a/vllm/model_executor/models/qwen2_5_vl.py b/vllm/model_executor/models/qwen2_5_vl.py
index f05f130e1c44..b0dcb55898b7 100644
--- a/vllm/model_executor/models/qwen2_5_vl.py
+++ b/vllm/model_executor/models/qwen2_5_vl.py
@@ -396,7 +396,7 @@ class Qwen2_5_VisionAttention(nn.Module):
         q, k, v = self.split_qkv(x)
         batch_size = q.shape[1]
 
-        q, k, v = (rearrange(x, "s b ... -> b s ...").contiguous() for x in (q, k, v))
+        q, k, v = (rearrange(x, "s b ... -> b s ...") for x in (q, k, v))
         if rotary_pos_emb is not None:
             # [2 * b, s, heads, head_dim]
             qk_concat = torch.cat([q, k], dim=0)
diff --git a/vllm/model_executor/models/qwen2_vl.py b/vllm/model_executor/models/qwen2_vl.py
index 821a9d13dc6f..2f74d4489cee 100644
--- a/vllm/model_executor/models/qwen2_vl.py
+++ b/vllm/model_executor/models/qwen2_vl.py
@@ -423,7 +423,7 @@ class Qwen2VisionAttention(nn.Module):
         q, k, v = self.split_qkv(x)
         batch_size = q.shape[1]
 
-        q, k, v = (rearrange(x, "s b ... -> b s ...").contiguous() for x in (q, k, v))
+        q, k, v = (rearrange(x, "s b ... -> b s ...") for x in (q, k, v))
         if rotary_pos_emb is not None:
             # [2 * b, s, heads, head_dim]
             qk_concat = torch.cat([q, k], dim=0)