diff --git a/vllm_ascend/attention/mla_v1.py b/vllm_ascend/attention/mla_v1.py
index 2d522e4..fabf95e 100644
--- a/vllm_ascend/attention/mla_v1.py
+++ b/vllm_ascend/attention/mla_v1.py
@@ -476,9 +476,11 @@ class AscendMLAImpl(MLAAttentionImpl):
             [self.qk_nope_head_dim, self.v_head_dim], dim=-1)
 
         # Convert from (L, N, V) to (N, L, V)
-        self.W_UV = W_UV.transpose(0, 1)
+        self.W_UV = W_UV.transpose(0, 1).contiguous()
         # Convert from (L, N, P) to (N, P, L)
-        self.W_UK_T = W_UK.permute(1, 2, 0)
+        self.W_UK_T = W_UK.permute(1, 2, 0).contiguous()
+        self.W_UV.data = torch_npu.npu_format_cast(self.W_UV.data, 29)
+        self.W_UK_T.data = torch_npu.npu_format_cast(self.W_UK_T.data, 29)
 
     def _forward_prefill(
         self,
diff --git a/vllm_ascend/quantization/w8a8.py b/vllm_ascend/quantization/w8a8.py
index f740d8f..db23cb0 100644
--- a/vllm_ascend/quantization/w8a8.py
+++ b/vllm_ascend/quantization/w8a8.py
@@ -110,5 +110,6 @@ class AscendW8A8LinearMethod:
             requires_grad=False).to(layer.aclnn_input_scale.dtype)
         if self.transpose_weight:
             layer.weight.data = layer.weight.data.transpose(0, 1).contiguous()
+        layer.weight.data = torch_npu.npu_format_cast(layer.weight.data, 29)
         layer.weight_scale.data = torch.flatten(layer.weight_scale.data)
         layer.weight_offset.data = torch.flatten(layer.weight_offset.data)