diff --git a/python/sglang/srt/layers/moe/fused_moe_triton/layer.py b/python/sglang/srt/layers/moe/fused_moe_triton/layer.py
index 59ca3f9cc..640e2b504 100644
--- a/python/sglang/srt/layers/moe/fused_moe_triton/layer.py
+++ b/python/sglang/srt/layers/moe/fused_moe_triton/layer.py
@@ -70,6 +70,7 @@ from sglang.srt.utils import (
     get_bool_env_var,
     is_cpu,
     is_hip,
+    is_npu,
     print_info_once,
     round_up,
 )
@@ -78,6 +79,7 @@ from sglang.srt.utils.custom_op import register_custom_op
 _is_hip = is_hip()
 _is_cpu_amx_available = cpu_has_amx_support()
 _is_cpu = is_cpu()
+_is_npu = is_npu()
 _use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip
 
 
@@ -778,6 +780,13 @@ class FusedMoE(torch.nn.Module):
         # expert weights into block layout. During weight update, we must restore
         # canonical load-time shapes before copying checkpoint tensors.
         if isinstance(method, UnquantizedFusedMoEMethod):
+            if _is_npu:
+                if weight_name.endswith(".experts.w2_weight"):
+                    if param.data.shape[1] != loaded_weight.shape[0]:
+                        param.data = param.data.transpose(1, 2).contiguous()
+                if weight_name.endswith(".experts.w13_weight"):
+                    if param.data.shape[2] != loaded_weight.shape[1]:
+                        param.data = param.data.transpose(1, 2).contiguous()
             method.maybe_restore_flashinfer_trtllm_bf16_weight_shape_for_load(
                 layer=self,
                 param=param,