diff --git a/tests/ut/eplb/core/test_eplb_device_transfer_loader.py b/tests/ut/eplb/core/test_eplb_device_transfer_loader.py
index 6c6ff263..3284e345 100644
--- a/tests/ut/eplb/core/test_eplb_device_transfer_loader.py
+++ b/tests/ut/eplb/core/test_eplb_device_transfer_loader.py
@@ -31,7 +31,8 @@ def mock_adaptor():
 
 
 def test_generate_task_and_state_flow(mock_adaptor):
-    loader_obj = loader.D2DExpertWeightLoader()
+    with patch("vllm_ascend.eplb.core.eplb_device_transfer_loader.get_dynamic_eplb_group", return_value=None):
+        loader_obj = loader.D2DExpertWeightLoader()
     loader_obj.set_adator(mock_adaptor)
 
     with patch("torch.distributed.P2POp") as mock_p2p, \
@@ -52,7 +53,8 @@ def test_generate_task_and_state_flow(mock_adaptor):
 
 
 def test_asyn_transfer_and_update(mock_adaptor):
-    loader_obj = loader.D2DExpertWeightLoader()
+    with patch("vllm_ascend.eplb.core.eplb_device_transfer_loader.get_dynamic_eplb_group", return_value=None):
+        loader_obj = loader.D2DExpertWeightLoader()
     loader_obj.set_adator(mock_adaptor)
 
     loader_obj.comm_op_list = ["fake_op"]
@@ -88,14 +90,16 @@ def test_asyn_transfer_and_update(mock_adaptor):
 
 
 def test_set_log2phy_map(mock_adaptor):
-    loader_obj = loader.D2DExpertWeightLoader()
+    with patch("vllm_ascend.eplb.core.eplb_device_transfer_loader.get_dynamic_eplb_group", return_value=None):
+        loader_obj = loader.D2DExpertWeightLoader()
     loader_obj.set_adator(mock_adaptor)
     loader_obj.set_log2phy_map({"a": 1})
     assert loader_obj.updated_log2phy_map == {"a": 1}
 
 
 def test_invalid_state_asyn_update(mock_adaptor):
-    loader_obj = loader.D2DExpertWeightLoader()
+    with patch("vllm_ascend.eplb.core.eplb_device_transfer_loader.get_dynamic_eplb_group", return_value=None):
+        loader_obj = loader.D2DExpertWeightLoader()
     loader_obj.set_adator(mock_adaptor)
 
     loader_obj.state = loader.ExpertWeightUpdateState.WAITING
diff --git a/vllm_ascend/distributed/parallel_state.py b/vllm_ascend/distributed/parallel_state.py
index e4a7c399..5f1c37f1 100644
--- a/vllm_ascend/distributed/parallel_state.py
+++ b/vllm_ascend/distributed/parallel_state.py
@@ -275,7 +275,7 @@ def get_fc3_quant_x_group() -> GroupCoordinator:
 
 
 def get_dynamic_eplb_group() -> GroupCoordinator:
-    assert _DYNAMIC_EPLB is not None, "fc3 quant x group is not initialized"
+    assert _DYNAMIC_EPLB is not None, "Dynamic eplb group is not initialized"
     return _DYNAMIC_EPLB
 
 
diff --git a/vllm_ascend/eplb/core/eplb_device_transfer_loader.py b/vllm_ascend/eplb/core/eplb_device_transfer_loader.py
index 728a61f2..79321aca 100644
--- a/vllm_ascend/eplb/core/eplb_device_transfer_loader.py
+++ b/vllm_ascend/eplb/core/eplb_device_transfer_loader.py
@@ -19,6 +19,8 @@ from enum import Enum
 import torch.distributed as dist
 from vllm.logger import logger
 
+from vllm_ascend.distributed.parallel_state import get_dynamic_eplb_group
+
 
 class ExpertWeightUpdateState(Enum):
     WAITING = 0  # waiting for updated expert_map by EplbWorker
@@ -35,6 +37,7 @@ class D2DExpertWeightLoader:
         self.state = ExpertWeightUpdateState.WAITING
         self.recv_expert_list = []
         self.num_layers = 0
+        self.comm_group = get_dynamic_eplb_group()
 
     def set_adator(self, eplb_adaptor):
         self.eplb_adaptor = eplb_adaptor
@@ -53,12 +56,16 @@ class D2DExpertWeightLoader:
             dst_rank, global_expert_id_to_send = send_info
             local_expert_id = self.eplb_adaptor.expert_map_per_layer_cpu[layer_id][global_expert_id_to_send].item()
             for src_tensor in self.eplb_adaptor.expert_param_per_layer[layer_id][local_expert_id]:
-                self.comm_op_list.append(dist.P2POp(dist.isend, src_tensor, dst_rank))
+                self.comm_op_list.append(
+                    dist.P2POp(dist.isend, src_tensor, dst_rank, group=self.comm_group.device_group)
+                )
 
         for buffer_tensor_id, recv_info in enumerate(expert_recv_info):
             recv_rank, global_expert_id_to_recv = recv_info
             for buffer_tensor in self.eplb_adaptor.buffer_tensor_list[buffer_tensor_id]:
-                self.comm_op_list.append(dist.P2POp(dist.irecv, buffer_tensor, recv_rank))
+                self.comm_op_list.append(
+                    dist.P2POp(dist.irecv, buffer_tensor, recv_rank, group=self.comm_group.device_group)
+                )
             local_expert_to_replace = self.updated_expert_map[global_expert_id_to_recv].item()
             self.recv_expert_list.append((local_expert_to_replace, buffer_tensor_id))
 
diff --git a/vllm_ascend/eplb/eplb_updator.py b/vllm_ascend/eplb/eplb_updator.py
index fa06737d..285bd486 100644
--- a/vllm_ascend/eplb/eplb_updator.py
+++ b/vllm_ascend/eplb/eplb_updator.py
@@ -155,12 +155,12 @@ class EplbUpdator:
         for dst_rank in range(self.world_size):
             if dst_rank == self.rank_id:
                 continue
-            comm_op_list.append(dist.P2POp(dist.isend, src_tensor, dst_rank))
+            comm_op_list.append(dist.P2POp(dist.isend, src_tensor, dst_rank, group=self.comm_group.device_group))
 
         for src_rank in range(self.world_size):
             if src_rank == self.rank_id:
                 continue
-            comm_op_list.append(dist.P2POp(dist.irecv, src_tensor, src_rank))
+            comm_op_list.append(dist.P2POp(dist.irecv, src_tensor, src_rank, group=self.comm_group.device_group))
         if comm_op_list:
             reqs = dist.batch_isend_irecv(comm_op_list)
 
diff --git a/vllm_ascend/utils.py b/vllm_ascend/utils.py
index 458b95cd..1d341899 100644
--- a/vllm_ascend/utils.py
+++ b/vllm_ascend/utils.py
@@ -62,7 +62,7 @@ _CP_CHUNKEDPREFILL_COMM_STREAM = None
 _ASCEND_CUSTOMOP_IS_REIGISTERED = False
 _DEFAULT_BUFFER_SIZE = 200
 _MIN_DP_BUFFER_SIZE = 50
-_DYNAMIC_EPLB_BUFFER_SIZE = 1  # num_experts * num_layers * 64 byte
+_DYNAMIC_EPLB_BUFFER_SIZE = 100
 _IS_MOE_MODEL = None
 _IS_DRAFTER_MOE_MODEL = None
 _IS_VL_MODEL = None