diff --git a/python/sglang/srt/hardware_backend/npu/attention/ascend_backend.py b/python/sglang/srt/hardware_backend/npu/attention/ascend_backend.py
index 22d9021f286c..2c804058a16f 100644
--- a/python/sglang/srt/hardware_backend/npu/attention/ascend_backend.py
+++ b/python/sglang/srt/hardware_backend/npu/attention/ascend_backend.py
@@ -297,6 +297,8 @@ def init_forward_metadata(self, forward_batch: ForwardBatch):
         if (
             self.use_mla
             and forward_batch.forward_mode.is_extend()
+            and not forward_batch.forward_mode.is_draft_extend(include_v2=True)
+            and not forward_batch.forward_mode.is_target_verify()
             and sum(forward_batch.extend_prefix_lens_cpu) > 0
         ):
             self.forward_metadata.prefix_lens = forward_batch.extend_prefix_lens.to(
diff --git a/python/sglang/srt/hardware_backend/npu/quantization/fused_moe_method_npu.py b/python/sglang/srt/hardware_backend/npu/quantization/fused_moe_method_npu.py
index 938314b0f425..279700b3e542 100644
--- a/python/sglang/srt/hardware_backend/npu/quantization/fused_moe_method_npu.py
+++ b/python/sglang/srt/hardware_backend/npu/quantization/fused_moe_method_npu.py
@@ -567,11 +567,13 @@ def apply_without_routing_weights(
         group_list,
         output_dtype,
     ):
+        from sgl_kernel_npu.activation.swiglu_quant import swiglu_quant
+
         hidden_states = torch.ops.npu.npu_grouped_matmul(
             x=[hidden_states],
-            weight=[self.w13_weight],
-            scale=[self.w13_weight_scale],
-            bias=[self.w13_scale_bias],
+            weight=[layer.w13_weight],
+            scale=[layer.w13_weight_scale],
+            bias=[layer.w13_scale_bias],
             per_token_scale=[hidden_states_scale],
             group_list=group_list,
             split_item=2,
@@ -580,15 +582,15 @@ def apply_without_routing_weights(
             output_dtype=output_dtype,
         )[0]
 
-        # act_fn: swiglu
-        hidden_states = torch.ops.npu.npu_swiglu(hidden_states)
-        hidden_states, swiglu_out_scale = torch.ops.npu.npu_dynamic_quant(hidden_states)
+        hidden_states, swiglu_out_scale = swiglu_quant(
+            hidden_states, group_list, group_list_type
+        )
 
         hidden_states = torch.ops.npu.npu_grouped_matmul(
             x=[hidden_states],
-            weight=[self.w2_weight],
-            scale=[self.w2_weight_scale],
-            bias=[self.w2_scale_bias],
+            weight=[layer.w2_weight],
+            scale=[layer.w2_weight_scale],
+            bias=[layer.w2_scale_bias],
             per_token_scale=[swiglu_out_scale],
             group_list=group_list,
             split_item=2,
diff --git a/python/sglang/srt/layers/moe/ep_moe/layer.py b/python/sglang/srt/layers/moe/ep_moe/layer.py
index 97e65433144d..53c832e1d1bd 100644
--- a/python/sglang/srt/layers/moe/ep_moe/layer.py
+++ b/python/sglang/srt/layers/moe/ep_moe/layer.py
@@ -6,6 +6,9 @@
 import torch
 
 from sglang.srt.environ import envs
+from sglang.srt.hardware_backend.npu.quantization.fused_moe_method_npu import (
+    NPUW4A16Int4DynamicMoEMethod,
+)
 from sglang.srt.layers import deep_gemm_wrapper
 from sglang.srt.layers.moe import (
     get_deepep_mode,
@@ -347,7 +350,9 @@ def forward_npu(
                 )
             else:
                 input_quant = get_bool_env_var("DEEP_NORMAL_MODE_USE_INT8_QUANT")
-                if not input_quant and self.w13_weight.dtype != torch.int32:
+                if not input_quant and not isinstance(
+                    self.quant_method, NPUW4A16Int4DynamicMoEMethod
+                ):
                     hidden_states, hidden_states_scale = torch_npu.npu_dynamic_quant(
                         hidden_states
                     )