diff --git a/vllm_ascend/models/deepseek_v2.py b/vllm_ascend/models/deepseek_v2.py
index bfa86f0ee2..2d48f493a1 100644
--- a/vllm_ascend/models/deepseek_v2.py
+++ b/vllm_ascend/models/deepseek_v2.py
@@ -68,6 +68,7 @@
     make_empty_intermediate_tensors_factory, make_layers, maybe_prefix)
 from vllm.sequence import IntermediateTensors
 
+import vllm_ascend.envs as envs_ascend
 from vllm_ascend.ascend_config import get_ascend_config
 from vllm_ascend.distributed.parallel_state import get_ep_group
 from vllm_ascend.ops.fused_moe import AscendFusedMoE
@@ -407,8 +408,12 @@ def forward(self,
             experts_hidden_states[0] * self.routed_scaling_factor +
             experts_hidden_states[1])
         if self.all_reduce_merge:
-            # When all_reduce_merge is in progress, shared_experts does not do all_reduce in mlp, but waits until shared_experts+router_experts are completed before doing all_reduce
-            hidden_states = tensor_model_parallel_all_reduce(hidden_states)
+            if envs_ascend.VLLM_ENABLE_FUSED_EXPERTS_ALLGATHER_EP and not is_prefill:
+                # Prefill uses the AllGatherEP solution (using the VLLM_ENABLE_FUSED_EXPERTS_ALLGATHER_EP switch), and Decode uses the MC2 solution.
+                ...
+            else:
+                # When all_reduce_merge is in progress, shared_experts does not do all_reduce in mlp, but waits until shared_experts+router_experts are completed before doing all_reduce
+                hidden_states = tensor_model_parallel_all_reduce(hidden_states)
 
         return hidden_states
 
diff --git a/vllm_ascend/ops/fused_moe.py b/vllm_ascend/ops/fused_moe.py
index 1221d8984d..1b0368a3a5 100644
--- a/vllm_ascend/ops/fused_moe.py
+++ b/vllm_ascend/ops/fused_moe.py
@@ -1418,6 +1418,14 @@ def forward(self,
             final_hidden_states = tensor_model_parallel_all_reduce(
                 final_hidden_states)
 
+        if tp_size > 1 and envs_ascend.VLLM_ENABLE_FUSED_EXPERTS_ALLGATHER_EP and self.all_reduce_merge and fused_moe_state in [
+                FusedMoEState.MC2
+        ]:
+            # Prefill uses the AllGatherEP solution (using the VLLM_ENABLE_FUSED_EXPERTS_ALLGATHER_EP switch), and Decode uses the MC2 solution.
+            # This solution uses the all_reduce_merge optimization in Prefill, but does not use the all_reduce_merge optimization in the decode part.
+            shared_hidden_states = tensor_model_parallel_all_reduce(
+                shared_hidden_states)
+
         if shared_experts:
             return final_hidden_states, shared_hidden_states
         else:
diff --git a/vllm_ascend/utils.py b/vllm_ascend/utils.py
index 634e13cb9e..7beee243f1 100644
--- a/vllm_ascend/utils.py
+++ b/vllm_ascend/utils.py
@@ -458,6 +458,10 @@ def get_rm_router_logits_state(ep_size: int, dp_size: int,
 # TODO(ttanzhiqiang): all_reduce merge
 # When all_reduce_merge is in progress, shared_experts does not do all_reduce in mlp, but waits until shared_experts+router_experts are completed before doing all_reduce
 # Currently, all_reduce_merge is enabled by default in the AllGather, AllGatherEP and NaiveMulticast scenarios of the deepseek model.
+# 1. If Prefill/decode use AllGather or NaiveMulticast solution at the same time, this logic is normal, and this solution is used for optimization
+# 2. If Prefill/decode use All2All/MC2 solution at the same time, this logic is also normal, and this solution is not used for optimization
+# 3. Prefill uses AllGatherEP solution (use VLLM_ENABLE_FUSED_EXPERTS_ALLGATHER_EP switch), and Decode uses MC2 solution. (Prefill can be merged/Prefill and Decode strategies are different and cannot be merged)
+# 4. In the PD separation scenario, the strategies used by P and D are separate, so there will be no impact.
 def get_all_reduce_merge_state(ep_size: int, is_deepseek_v3_r1: bool):
     # the fusion operator torch_npu.npu_grouped_matmul_finalize_routing called by allgather ep
     # only supports deepseek v3/r1