diff --git a/vllm_ascend/eplb/eplb_updator.py b/vllm_ascend/eplb/eplb_updator.py
index f5072826..89721702 100644
--- a/vllm_ascend/eplb/eplb_updator.py
+++ b/vllm_ascend/eplb/eplb_updator.py
@@ -23,8 +23,6 @@ from vllm.logger import logger
 
 from vllm_ascend.eplb.core.eplb_utils import EPLBParamUtils
 from vllm_ascend.eplb.core.eplb_worker import EplbProcess
-from vllm_ascend.eplb.utils import moe_load_async_stream
-from vllm_ascend.utils import npu_stream_switch
 
 
 class EplbUpdator:
@@ -155,22 +153,21 @@ class EplbUpdator:
 
         self._gather_buffer = None
         if dist.is_initialized():
-            with npu_stream_switch(moe_load_async_stream()):
-                self.world_size = dist.get_world_size()
-                self.device = local_load.device
-                if self._gather_buffer is None:
-                    shape = (self.world_size, *local_load.shape)
-                    self._gather_buffer = torch.empty(shape,
-                                                      dtype=local_load.dtype,
-                                                      device=self.device)
+            self.world_size = dist.get_world_size()
+            self.device = local_load.device
+            if self._gather_buffer is None:
+                shape = (self.world_size, *local_load.shape)
+                self._gather_buffer = torch.empty(shape,
+                                                  dtype=local_load.dtype,
+                                                  device=self.device)
 
-                dist.all_gather_into_tensor(self._gather_buffer, local_load)
+            dist.all_gather_into_tensor(self._gather_buffer, local_load)
 
-                moe_load = self._gather_buffer.permute(1, 0, 2)
-                self.shared_dict["moe_load"] = moe_load.cpu()
-                logger.debug(
-                    f"[ModelRunner] Updated shared_dict['moe_load'] shape={moe_load.shape}"
-                )
+            moe_load = self._gather_buffer.permute(1, 0, 2)
+            self.shared_dict["moe_load"] = moe_load.cpu()
+            logger.debug(
+                f"[ModelRunner] Updated shared_dict['moe_load'] shape={moe_load.shape}"
+            )
         else:
             moe_load = local_load.unsqueeze(1)
             self.shared_dict["moe_load"] = moe_load.cpu()
diff --git a/vllm_ascend/eplb/utils.py b/vllm_ascend/eplb/utils.py
index 6f703f10..0efa623b 100644
--- a/vllm_ascend/eplb/utils.py
+++ b/vllm_ascend/eplb/utils.py
@@ -18,9 +18,6 @@
 import types
 
 import torch
-import torch_npu
-
-_MOE_LOAD_ASYNC_STREAM = None
 
 
 def get_expert_map(self, layer_id):
@@ -78,12 +75,3 @@ def model_register(model, model_config):
         model.num_moe_layers = config.num_hidden_layers - model.num_dense_layers
     else:
         raise NotImplementedError("EPLB is not supported.")
-
-
-def moe_load_async_stream() -> torch_npu.npu.Stream:
-    global _MOE_LOAD_ASYNC_STREAM
-    if _MOE_LOAD_ASYNC_STREAM is None:
-        # when this function is called before any stream is set,
-        # we return the default stream.
-        _MOE_LOAD_ASYNC_STREAM = torch_npu.npu.Stream()
-    return _MOE_LOAD_ASYNC_STREAM
\ No newline at end of file
diff --git a/vllm_ascend/ops/fused_moe/fused_moe.py b/vllm_ascend/ops/fused_moe/fused_moe.py
index f9534515..d2a55e31 100644
--- a/vllm_ascend/ops/fused_moe/fused_moe.py
+++ b/vllm_ascend/ops/fused_moe/fused_moe.py
@@ -32,7 +32,6 @@ from vllm_ascend.ascend_config import get_ascend_config
 from vllm_ascend.ascend_forward_context import MoECommType
 from vllm_ascend.distributed.parallel_state import get_mc2_group
 from vllm_ascend.eplb.core.eplb_utils import init_eplb_config
-from vllm_ascend.eplb.utils import moe_load_async_stream
 from vllm_ascend.flash_common3_context import (get_flash_common3_context,
                                                set_flash_common3_context)
 from vllm_ascend.ops.fused_moe.experts_selector import (select_experts,
@@ -371,13 +370,8 @@ class AscendFusedMoE(FusedMoE):
             group_list_type = fused_experts_results.group_list_type
             assert expert_tokens is not None and group_list_type is not None, \
                 "expert_tokens and group_list_type should not be None when dynamic_eplb is enabled."
-            moe_load_stream = moe_load_async_stream()
-            cur_stream = torch.npu.current_stream()
-            moe_load_stream.wait_stream(cur_stream)
-            with npu_stream_switch(moe_load_stream):
-                self.moe_load += expert_tokens if group_list_type == 1 else \
-                    torch.cat([expert_tokens[:1], expert_tokens[1:] - expert_tokens[:-1]])
-            cur_stream.wait_stream(moe_load_stream)
+            self.moe_load += expert_tokens if group_list_type == 1 else \
+                torch.cat([expert_tokens[:1], expert_tokens[1:] - expert_tokens[:-1]])
 
         routed_out = forward_context.moe_comm_method.finalize(
             hidden_states=fused_experts_results.routed_out,