diff --git a/fastdeploy/model_executor/layers/linear.py b/fastdeploy/model_executor/layers/linear.py
index 9b4bfe840..2c7f9aef3 100644
--- a/fastdeploy/model_executor/layers/linear.py
+++ b/fastdeploy/model_executor/layers/linear.py
@@ -415,6 +415,7 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
     def weight_loader(self, param, loaded_weight, loaded_shard_id: Optional[str] = None):
         model_format = getattr(param, "model_format", "")
         if model_format == "torch":
+            loaded_weight = get_tensor(loaded_weight)
             loaded_weight = loaded_weight.transpose([1, 0])
         output_dim = getattr(param, "output_dim", None)
         assert output_dim is not None
@@ -446,7 +447,7 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
                 shard_offset = self.local_rank * block_size
                 shard_size = (self.local_rank + 1) * block_size
                 loaded_weight = slice_fn(loaded_weight, output_dim, start=shard_offset, end=shard_size)
-
+            loaded_weight = get_tensor(loaded_weight)
             if not param._is_initialized():
                 param.initialize()
             param_shard_size = output_size // 2
@@ -548,6 +549,7 @@ class QKVParallelLinear(ColumnParallelLinear):
         head_dim = param.shape[dim] // (self.num_heads_per_rank + 2 * self.kv_num_heads_per_rank)
         model_format = getattr(param, "model_format", "")
         if model_format == "torch":
+            loaded_weight = get_tensor(loaded_weight)
             loaded_weight = loaded_weight.transpose([1, 0])
         if loaded_shard_id is None:
             # Loaded weight is already fused on disk
@@ -568,12 +570,13 @@ class QKVParallelLinear(ColumnParallelLinear):
             # Tensor parallelism splits the weight along the output_dim
             if self.nranks != 1:
                 block_size = self._get_shard_size_mapping(loaded_shard_id)
-                dim = -1 if output_dim else 0
                 shard_id = self.local_rank if loaded_shard_id == "q" else self.local_rank // self.num_kv_head_replicas
                 shard_offset = shard_id * block_size
                 shard_size = (shard_id + 1) * block_size
                 loaded_weight = slice_fn(loaded_weight, output_dim, start=shard_offset, end=shard_size)
 
+            loaded_weight = get_tensor(loaded_weight)
+
             if not param._is_initialized():
                 param.initialize()
 
diff --git a/fastdeploy/model_executor/layers/moe/moe.py b/fastdeploy/model_executor/layers/moe/moe.py
index c77379e68..bc58ef3eb 100644
--- a/fastdeploy/model_executor/layers/moe/moe.py
+++ b/fastdeploy/model_executor/layers/moe/moe.py
@@ -176,17 +176,24 @@ class FusedMoE(nn.Layer):
 
         if shard_id is None:
             # 1.gate up fused in disk
+            model_format = getattr(param, "model_format", "")
+            is_torch_model = model_format == "torch"
             output_size = param[expert_id - self.expert_id_offset].shape[SHARD_ID_TO_SHARDED_DIM["gate"]]
-            shard_offsets = [
-                # (shard_id, shard_offset, shard_size)
-                ("gate", 0, output_size // 2 * self.tp_size),
-                ("up", output_size // 2 * self.tp_size, output_size // 2 * self.tp_size),
-            ]
-            for shard_id, shard_offset, shard_size in shard_offsets:
-                loaded_weight_shard = slice_fn(
-                    loaded_weight, SHARD_ID_TO_SHARDED_DIM[shard_id], shard_offset, shard_offset + shard_size
-                )
-                self.weight_loader(param, loaded_weight_shard, expert_id, shard_id)
+            per_rank = output_size // 2
+            start = self.tp_rank * per_rank
+            loaded_weight_shard_gate = slice_fn(
+                loaded_weight, is_torch_model ^ SHARD_ID_TO_SHARDED_DIM["gate"], start, start + per_rank
+            )
+            self._load_gate_up_weight(
+                param, expert_id, loaded_weight_shard_gate, "gate", SHARD_ID_TO_SHARDED_DIM["gate"], is_sharded=True
+            )
+            start_up = output_size // 2 * self.tp_size + self.tp_rank * per_rank
+            loaded_weight_shard_up = slice_fn(
+                loaded_weight, is_torch_model ^ SHARD_ID_TO_SHARDED_DIM["up"], start_up, start_up + per_rank
+            )
+            self._load_gate_up_weight(
+                param, expert_id, loaded_weight_shard_up, "up", SHARD_ID_TO_SHARDED_DIM["up"], is_sharded=True
+            )
         else:
             # 2.gate up splited in disk
             assert shard_id in ["gate", "down", "up"]
@@ -198,22 +205,23 @@ class FusedMoE(nn.Layer):
                 shard_dim=SHARD_ID_TO_SHARDED_DIM[shard_id],
             )
 
-    def _load_gate_up_weight(self, param, expert_id, loaded_weight, shard_id, shard_dim=None):
+    def _load_gate_up_weight(self, param, expert_id, loaded_weight, shard_id, shard_dim=None, is_sharded=False):
         model_format = getattr(param, "model_format", "")
-        if model_format == "torch":
-            loaded_weight = loaded_weight.transpose([1, 0])
-        dim = -1 if shard_dim else 0
-        if self.tp_size > 1:
+        is_torch_model = model_format == "torch"
+        if self.tp_size > 1 and not is_sharded:
+            tp_shard_dim = is_torch_model ^ shard_dim
+            weight_dim = -1 if tp_shard_dim else 0
             if isinstance(loaded_weight, (np.ndarray, paddle.Tensor)):
-                size = loaded_weight.shape[dim]
+                size = loaded_weight.shape[weight_dim]
             else:
-                size = loaded_weight.get_shape()[dim]
+                size = loaded_weight.get_shape()[weight_dim]
             block_size = size // self.tp_size
             shard_offset = self.tp_rank * block_size
             shard_size = (self.tp_rank + 1) * block_size
-            loaded_weight = slice_fn(loaded_weight, shard_dim, shard_offset, shard_size)
-
+            loaded_weight = slice_fn(loaded_weight, tp_shard_dim, shard_offset, shard_size)
+        loaded_weight = get_tensor(loaded_weight)
         expert_param = param[expert_id - self.expert_id_offset]
+        dim = -1 if shard_dim else 0
         param_shard_size = expert_param.shape[dim] // 2
         if shard_id == "gate":
             param_shard_offset = 0
@@ -232,9 +240,8 @@ class FusedMoE(nn.Layer):
             )
 
         # To ensure compatibility across backends, apply an extra transpose for GCU and XPU
-        if current_platform.is_xpu() or current_platform.is_gcu():
-            if expert_param.shape != loaded_weight.shape:
-                loaded_weight = loaded_weight.transpose([1, 0])
+        if expert_param.shape != loaded_weight.shape:
+            loaded_weight = loaded_weight.transpose([1, 0])
         assert expert_param.shape == loaded_weight.shape, (
             f"Attempted to load weight ({loaded_weight.shape}) " f"into parameter ({expert_param.shape})"
         )
@@ -242,26 +249,26 @@ class FusedMoE(nn.Layer):
 
     def _load_down_weight(self, param, expert_id, loaded_weight, shard_id, shard_dim=None):
         model_format = getattr(param, "model_format", "")
-        if model_format == "torch":
-            loaded_weight = loaded_weight.transpose([1, 0])
+        is_torch_model = model_format == "torch"
         if self.tp_size > 1 and shard_dim is not None:
-            dim = -1 if shard_dim else 0
-            if isinstance(loaded_weight, (np.ndarray, paddle.Tensor)):
+            tp_shard_dim = is_torch_model ^ shard_dim
+            dim = -1 if tp_shard_dim else 0
+            if isinstance(loaded_weight, paddle.Tensor):
                 size = loaded_weight.shape[dim]
             else:
                 size = loaded_weight.get_shape()[dim]
             block_size = size // self.tp_size
             shard_offset = self.tp_rank * block_size
             shard_size = (self.tp_rank + 1) * block_size
-            loaded_weight = slice_fn(loaded_weight, shard_dim, shard_offset, shard_size)
+            loaded_weight = slice_fn(loaded_weight, tp_shard_dim, shard_offset, shard_size)
+        loaded_weight = get_tensor(loaded_weight)
         expert_param = param[expert_id - self.expert_id_offset]
         if hasattr(param, "tensor_track"):
             # for dyn quant
             param.tensor_track.mark(start=0, batch_id=expert_id - self.expert_id_offset)
-        # To ensure compatibility across backends, apply an extra transpose for GCU and XPU
-        if current_platform.is_xpu or current_platform.is_gcu():
-            if expert_param.shape != loaded_weight.shape:
-                loaded_weight = loaded_weight.transpose([1, 0])
+        # To ensure compatibility across backends, apply an extra transpose for GCU and XPU and opensource weight
+        if expert_param.shape != loaded_weight.shape:
+            loaded_weight = loaded_weight.transpose([1, 0])
         assert expert_param.shape == loaded_weight.shape, (
             f"Attempted to load weight ({loaded_weight.shape}) " f"into parameter ({expert_param.shape})"
         )
diff --git a/fastdeploy/model_executor/load_weight_utils.py b/fastdeploy/model_executor/load_weight_utils.py
index 6e1097d86..be0d76a33 100644
--- a/fastdeploy/model_executor/load_weight_utils.py
+++ b/fastdeploy/model_executor/load_weight_utils.py
@@ -29,7 +29,6 @@ from safetensors import safe_open
 from tqdm import tqdm
 
 from fastdeploy.config import FDConfig
-from fastdeploy.model_executor.layers.utils import get_tensor
 from fastdeploy.model_executor.models.tp_utils import (
     check_tensor_parallel_prerequisites,
 )
@@ -186,8 +185,7 @@ def fast_weights_iterator(safe_tensor_list: list[str]):
         with fast_safe_open(st_file, framework="np") as f:
             for name in f.keys():
                 param_slice = f.get_slice(name)
-                paddle_tensor = get_tensor(param_slice)
-                yield name, paddle_tensor
+                yield name, param_slice
 
 
 def fastsafetensors_weights_iterator(
diff --git a/fastdeploy/model_executor/utils.py b/fastdeploy/model_executor/utils.py
index c95a45438..8e90fb80f 100644
--- a/fastdeploy/model_executor/utils.py
+++ b/fastdeploy/model_executor/utils.py
@@ -160,6 +160,7 @@ def default_weight_loader(fd_config: FDConfig) -> None:
         output_dim = getattr(param, "output_dim", None)
         model_format = getattr(param, "model_format", "")
         if model_format == "torch":
+            loaded_weight = get_tensor(loaded_weight)
             loaded_weight = loaded_weight.transpose([1, 0])
         # Tensor parallelism splits the weight along the output_dim
         if output_dim is not None and fd_config.parallel_config.tensor_parallel_size > 1: