diff --git a/vllm_ascend/attention/sfa_v1.py b/vllm_ascend/attention/sfa_v1.py
index 692566bf..692b3444 100644
--- a/vllm_ascend/attention/sfa_v1.py
+++ b/vllm_ascend/attention/sfa_v1.py
@@ -445,6 +445,13 @@ class AscendSFAImpl(MLAAttentionImpl):
         # Enable layer sharding via DSA-CP on the P node in the PD-disaggregated setup.
         self.enable_dsa_cp_with_layer_shard = enable_dsa_cp_with_layer_shard()
 
+        # Improves glm5 accuracy after enabling dsa-cp in scenarios with strict accuracy requirements,
+        # especially for customized cases, at the cost of performance degradation due to extra communication.
+        self.enable_dsa_cp_strict_accuracy = (
+            self.enable_dsa_cp_with_layer_shard
+            and self.vllm_config.model_config.hf_config.model_type in ["glm_moe_dsa"]
+        )
+
         # use original TP o_proj weight in PD mix stage, and full gather
         # for o_proj weight for prefill stage.
         self.enable_dsa_cp_with_o_proj_tp = enable_dsa_cp_with_o_proj_tp()
@@ -1239,6 +1246,16 @@ class AscendSFAImpl(MLAAttentionImpl):
                 return result
             attn_output = result
 
+        if self.enable_dsa_cp_strict_accuracy:
+            send = (
+                attn_output.view(-1, self.tp_size, self.num_heads * self.v_head_dim)
+                .permute(1, 0, 2)
+                .reshape(-1, self.num_heads * self.v_head_dim)
+            )
+
+            attn_output = torch.empty_like(send)
+            torch.distributed.all_to_all_single(attn_output, send, group=get_tp_group().device_group)
+
         output[...] = self.o_proj(attn_output)[0]
 
         maybe_save_kv_layer_to_connector(layer_name, list(kv_cache))
diff --git a/vllm_ascend/ops/linear_op.py b/vllm_ascend/ops/linear_op.py
index 85d29d3f..ffc5fbba 100644
--- a/vllm_ascend/ops/linear_op.py
+++ b/vllm_ascend/ops/linear_op.py
@@ -663,7 +663,11 @@ def _get_row_parallel_op(
     | None
 ):
     if enable_dsa_cp_with_layer_shard() and "o_proj" in prefix:
-        return ShardedCPRowParallelOp(layer)
+        from vllm.config import get_current_vllm_config
+
+        vllm_config = get_current_vllm_config()
+        if vllm_config.model_config.hf_config.model_type not in ["glm_moe_dsa"]:
+            return ShardedCPRowParallelOp(layer)
     if "down_proj" in prefix and mlp_tp_enable() and not is_moe_layer(prefix):
         return MLPRowParallelOp(layer)
     if "o_proj" in prefix and oproj_tp_enable():