diff --git a/vllm_ascend/attention/sfa_v1.py b/vllm_ascend/attention/sfa_v1.py
index b74efc8d8b9..cc443f55b1a 100644
--- a/vllm_ascend/attention/sfa_v1.py
+++ b/vllm_ascend/attention/sfa_v1.py
@@ -470,7 +470,7 @@ def get_and_maybe_dequant_weights(layer: LinearBase):
             if self.fused_qkv_a_proj is None or not isinstance(
                     quant_method, AscendW8A8LinearMethod):
                 reasons.append(
-                    "Currently mlapo only supports W8A8 quantization in MLA scenario."
+                    "Currently mlapo only supports W8A8 quantization in SFA scenario."
                     "Some layers in your model are not quantized with W8A8,"
                     "thus mlapo is disabled for these layers.")
             if self.enable_sfa_cp:
@@ -597,8 +597,6 @@ def _process_weights_for_fused_mlapo(self, act_dtype: torch.dtype):
         q_a_proj_wt = self.fused_qkv_a_proj.weight.data[
             ..., :self.q_lora_rank].contiguous()
 
-        self.fused_qkv_a_proj.weight = None
-
         kv_a_proj_wt = kv_a_proj_wt.t().contiguous()
         kv_a_proj_wt = trans_rope_weight(kv_a_proj_wt, self.qk_rope_head_dim)
         kv_a_proj_wt = kv_a_proj_wt.t().contiguous()
@@ -673,9 +671,12 @@ def _process_weights_for_fused_mlapo(self, act_dtype: torch.dtype):
         self.ctkv_scale = torch.tensor([1], dtype=act_dtype, device=device)
         self.q_nope_scale = torch.tensor([1], dtype=act_dtype, device=device)
 
-        if self.vllm_config.kv_transfer_config is not None:
+        if self.vllm_config.kv_transfer_config is not None and \
+            self.vllm_config.kv_transfer_config.is_kv_consumer:
+            self.fused_qkv_a_proj.weight = None
             self.fused_qkv_a_proj.deq_scale = None
             self.fused_qkv_a_proj.quant_bias = None
+            self.q_proj.weight = None
             self.q_proj.deq_scale = None
             self.q_proj.quant_bias = None
             torch.npu.empty_cache()