diff --git a/vllm_ascend/attention/mla_v1.py b/vllm_ascend/attention/mla_v1.py
index e250fdbc..0646b41c 100644
--- a/vllm_ascend/attention/mla_v1.py
+++ b/vllm_ascend/attention/mla_v1.py
@@ -764,12 +764,22 @@ class AscendMLAImpl(MLAAttentionImpl):
         self.enable_mlapo = envs.VLLM_ASCEND_ENABLE_MLAPO
 
     def _v_up_proj(self, x):
-        # Convert from (B, N, L) to (N, B, L)
-        x = x.view(-1, self.num_heads, self.kv_lora_rank).transpose(0, 1)
-        # # Multiply (N, B, L) x (N, L, V) -> (N, B, V)
-        x = torch.bmm(x, self.W_UV)
-        # # Convert from (N, B, V) to (B, N * V)
-        x = x.transpose(0, 1).reshape(-1, self.num_heads * self.v_head_dim)
+        if x.dtype in [torch.float16, torch.bfloat16] \
+                and hasattr(torch.ops._C_ascend, "batch_matmul_transpose"):
+            x = x.view(-1, self.num_heads, self.kv_lora_rank)
+            b, _, _ = x.shape
+            res = torch.empty((b, self.num_heads, self.v_head_dim),
+                              dtype=x.dtype,
+                              device=x.device)
+            torch.ops._C_ascend.batch_matmul_transpose(x, self.W_UV, res)
+            x = res.reshape(-1, self.num_heads * self.v_head_dim)
+        else:
+            # Convert from (B, N, L) to (N, B, L)
+            x = x.view(-1, self.num_heads, self.kv_lora_rank).transpose(0, 1)
+            # Multiply (N, B, L) x (N, L, V) -> (N, B, V)
+            x = torch.bmm(x, self.W_UV)
+            # Convert from (N, B, V) to (B, N * V)
+            x = x.transpose(0, 1).reshape(-1, self.num_heads * self.v_head_dim)
         return x
 
     # Return `ql_nope`, `q_pe`