diff --git a/fastdeploy/config.py b/fastdeploy/config.py
index a2cc620a8cf..6ac5c12a05f 100644
--- a/fastdeploy/config.py
+++ b/fastdeploy/config.py
@@ -1588,10 +1588,6 @@ def check(self):
             f"be less than or equal to max_num_partial_prefills: {self.max_num_partial_prefills}"
         )
         assert self.scheduler_config.splitwise_role in ["mixed", "prefill", "decode"]
-        # TODO(@wufeisheng): TP and EP need to be supported simultaneously.
-        assert (self.parallel_config.tensor_parallel_size == 1 and self.parallel_config.expert_parallel_size >= 1) or (
-            self.parallel_config.tensor_parallel_size >= 1 and self.parallel_config.expert_parallel_size == 1
-        ), "TP and EP cannot be enabled at the same time"
 
         if not self.cache_config.enable_chunked_prefill:
             if not envs.ENABLE_V1_KVCACHE_SCHEDULER:
diff --git a/fastdeploy/model_executor/load_weight_utils.py b/fastdeploy/model_executor/load_weight_utils.py
index 4bc30b779f1..5f7839955ec 100644
--- a/fastdeploy/model_executor/load_weight_utils.py
+++ b/fastdeploy/model_executor/load_weight_utils.py
@@ -188,7 +188,7 @@ def load_reordered_experts(model_path: str, key_name: str):
             return weight
 
 
-def load_ep_checkpoint(model_path: str, fd_config: FDConfig, return_numpy: bool = False):
+def load_ep_checkpoint(cls: PretrainedModel, model_path: str, fd_config: FDConfig, return_numpy: bool = False):
     """
     load ep checkpoint
     """
@@ -266,6 +266,10 @@ def get_expert_ranges(fd_config):
         if k in weight_list:
             filtered_map[k] = weight_list[k]
 
+    if fd_config.parallel_config.tensor_parallel_size > 1:
+        tp_actions = cls._get_tensor_parallel_mappings(fd_config.model_config.pretrained_config)
+        new_actions = {k: v for k, v in tp_actions.items() if k not in num_local_ffn_keys}
+
     state_dict = {}
     # Get all safetensor file paths that need to be opened
     safetensor_paths = set(filtered_map.values())
@@ -281,6 +285,9 @@ def get_expert_ranges(fd_config):
             for k in filtered_map:
                 if filtered_map[k] == safetensor_path and k in f.keys():
                     weight = f.get_tensor(k)
+                    if fd_config.parallel_config.tensor_parallel_size > 1:
+                        if k in new_actions:
+                            weight = new_actions[k](weight)
                     if not return_numpy:
                         weight = paddle.Tensor(weight, zero_copy=True)
                         weight = weight._copy_to(paddle.framework._current_expected_place(), False)
@@ -456,13 +463,8 @@ def load_composite_checkpoint(
     # 2. Tensor Parallel (TP)
     # 3. Pre-sharded (pre-split)
     """
-    # (TODO: remove in the future)
-    if (
-        fd_config.parallel_config.use_ep
-        and fd_config.speculative_config.model_type != "mtp"
-        and fd_config.parallel_config.tensor_parallel_size == 1
-    ):
-        state_dict = load_ep_checkpoint(model_path, fd_config, return_numpy=True)
+    if fd_config.parallel_config.use_ep and fd_config.speculative_config.model_type != "mtp":
+        state_dict = load_ep_checkpoint(cls, model_path, fd_config, return_numpy=True)
     else:
         rank_dirs = [
             f for f in os.listdir(model_path) if f.startswith("rank") and os.path.isdir(os.path.join(model_path, f))
diff --git a/fastdeploy/worker/worker_process.py b/fastdeploy/worker/worker_process.py
index ab03d8ce427..6e58aa16386 100644
--- a/fastdeploy/worker/worker_process.py
+++ b/fastdeploy/worker/worker_process.py
@@ -720,7 +720,9 @@ def initialize_fd_config(args, ranks: int = 1, local_rank: int = 0) -> FDConfig:
         num_experts_per_rank = num_experts // parallel_config.expert_parallel_size
         num_experts_start_offset = expert_parallel_rank * num_experts_per_rank
         max_chips_per_node = 16 if current_platform.is_iluvatar() else 8
-        parallel_config.local_data_parallel_id = expert_parallel_rank % max_chips_per_node
+        parallel_config.local_data_parallel_id = parallel_config.data_parallel_rank % (
+            max_chips_per_node // parallel_config.tensor_parallel_size
+        )
 
         parallel_config.expert_parallel_rank = expert_parallel_rank
         parallel_config.num_experts_per_rank = num_experts_per_rank