diff --git a/vllm/model_executor/models/glm4_1v.py b/vllm/model_executor/models/glm4_1v.py
index 2c2f45c2453e..7a4fee76ae6b 100644
--- a/vllm/model_executor/models/glm4_1v.py
+++ b/vllm/model_executor/models/glm4_1v.py
@@ -797,13 +797,8 @@ class Glm4vVisionTransformer(nn.Module):
         # Use pre-computed cos_sin_cache from RotaryEmbedding
         cos, sin = self.rotary_pos_emb.get_cos_sin(max_grid_size)
 
-        cos_h = cos[pos_ids[:, 0]]  # (num_tokens, rotary_dim // 2)
-        cos_w = cos[pos_ids[:, 1]]
-        sin_h = sin[pos_ids[:, 0]]
-        sin_w = sin[pos_ids[:, 1]]
-
-        cos_combined = torch.cat([cos_h, cos_w], dim=-1)
-        sin_combined = torch.cat([sin_h, sin_w], dim=-1)
+        cos_combined = cos[pos_ids].flatten(1)
+        sin_combined = sin[pos_ids].flatten(1)
         return cos_combined, sin_combined, pos_ids
 
     def compute_attn_mask_seqlen(
diff --git a/vllm/model_executor/models/qwen2_5_vl.py b/vllm/model_executor/models/qwen2_5_vl.py
index 2e4fd9645d88..5b5d50ec8935 100644
--- a/vllm/model_executor/models/qwen2_5_vl.py
+++ b/vllm/model_executor/models/qwen2_5_vl.py
@@ -738,13 +738,8 @@ class Qwen2_5_VisionTransformer(nn.Module):
         # Use pre-computed cos_sin_cache from RotaryEmbedding
         cos, sin = self.rotary_pos_emb.get_cos_sin(max_size)
 
-        cos_h = cos[pos_ids[:, 0]]  # (num_tokens, rotary_dim // 2)
-        cos_w = cos[pos_ids[:, 1]]
-        sin_h = sin[pos_ids[:, 0]]
-        sin_w = sin[pos_ids[:, 1]]
-
-        cos_combined = torch.cat([cos_h, cos_w], dim=-1)
-        sin_combined = torch.cat([sin_h, sin_w], dim=-1)
+        cos_combined = cos[pos_ids].flatten(1)
+        sin_combined = sin[pos_ids].flatten(1)
 
         cos_combined = cos_combined.reshape(
             cos_combined.shape[0] // self.spatial_merge_unit,
diff --git a/vllm/model_executor/models/qwen2_vl.py b/vllm/model_executor/models/qwen2_vl.py
index 53df5972a8fe..cda8eaf5377f 100644
--- a/vllm/model_executor/models/qwen2_vl.py
+++ b/vllm/model_executor/models/qwen2_vl.py
@@ -724,13 +724,8 @@ class Qwen2VisionTransformer(nn.Module):
         # Use pre-computed cos_sin_cache from RotaryEmbedding
         cos, sin = self.rotary_pos_emb.get_cos_sin(max_grid_size)
 
-        cos_h = cos[pos_ids[:, 0]]  # (num_tokens, rotary_dim // 2)
-        cos_w = cos[pos_ids[:, 1]]
-        sin_h = sin[pos_ids[:, 0]]
-        sin_w = sin[pos_ids[:, 1]]
-
-        cos_combined = torch.cat([cos_h, cos_w], dim=-1)
-        sin_combined = torch.cat([sin_h, sin_w], dim=-1)
+        cos_combined = cos[pos_ids].flatten(1)
+        sin_combined = sin[pos_ids].flatten(1)
         return cos_combined, sin_combined
 
     def compute_attn_mask_seqlen(
diff --git a/vllm/model_executor/models/qwen3_omni_moe_thinker.py b/vllm/model_executor/models/qwen3_omni_moe_thinker.py
index 8274b92138f7..d2fd74a5e41a 100755
--- a/vllm/model_executor/models/qwen3_omni_moe_thinker.py
+++ b/vllm/model_executor/models/qwen3_omni_moe_thinker.py
@@ -428,13 +428,8 @@ class Qwen3Omni_VisionTransformer(nn.Module):
         # Use pre-computed cos_sin_cache from RotaryEmbedding
         cos, sin = self.rotary_pos_emb.get_cos_sin(max_grid_size)
 
-        cos_h = cos[pos_ids[:, 0]]  # (num_tokens, rotary_dim // 2)
-        cos_w = cos[pos_ids[:, 1]]
-        sin_h = sin[pos_ids[:, 0]]
-        sin_w = sin[pos_ids[:, 1]]
-
-        cos_combined = torch.cat([cos_h, cos_w], dim=-1)
-        sin_combined = torch.cat([sin_h, sin_w], dim=-1)
+        cos_combined = cos[pos_ids].flatten(1)
+        sin_combined = sin[pos_ids].flatten(1)
 
         return cos_combined, sin_combined
 
diff --git a/vllm/model_executor/models/qwen3_vl.py b/vllm/model_executor/models/qwen3_vl.py
index 99a4007ef7f2..0c546309400b 100644
--- a/vllm/model_executor/models/qwen3_vl.py
+++ b/vllm/model_executor/models/qwen3_vl.py
@@ -459,18 +459,13 @@ class Qwen3_VisionTransformer(nn.Module):
             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)
+        pos_ids = torch.cat(pos_ids, dim=0).to(self.device, non_blocking=True)
 
         # Use pre-computed cos_sin_cache from RotaryEmbedding
         cos, sin = self.rotary_pos_emb.get_cos_sin(max_grid_size)
 
-        cos_h = cos[pos_ids[:, 0]]  # (num_tokens, rotary_dim // 2)
-        cos_w = cos[pos_ids[:, 1]]
-        sin_h = sin[pos_ids[:, 0]]
-        sin_w = sin[pos_ids[:, 1]]
-
-        cos_combined = torch.cat([cos_h, cos_w], dim=-1)
-        sin_combined = torch.cat([sin_h, sin_w], dim=-1)
+        cos_combined = cos[pos_ids].flatten(1)
+        sin_combined = sin[pos_ids].flatten(1)
 
         return cos_combined, sin_combined
 
@@ -566,12 +561,6 @@ class Qwen3_VisionTransformer(nn.Module):
         pos_embeds = self.fast_pos_embed_interpolate(grid_thw_list)
         hidden_states = hidden_states + pos_embeds
         rotary_pos_emb_cos, rotary_pos_emb_sin = self.rot_pos_emb(grid_thw_list)
-        rotary_pos_emb_cos = rotary_pos_emb_cos.to(
-            hidden_states.device, non_blocking=True
-        )
-        rotary_pos_emb_sin = rotary_pos_emb_sin.to(
-            hidden_states.device, non_blocking=True
-        )
 
         cu_seqlens = torch.repeat_interleave(
             grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]