diff --git a/python/sglang/srt/models/llada2.py b/python/sglang/srt/models/llada2.py
index 5fa3f64d8..db97e2958 100644
--- a/python/sglang/srt/models/llada2.py
+++ b/python/sglang/srt/models/llada2.py
@@ -96,6 +96,15 @@ logger = logging.getLogger(__name__)
 _is_cuda = is_cuda()
 _is_npu = is_npu()
 
+split_qkv_rmsnorm_rope_pos_cache_half_npu = None
+if _is_npu:
+    try:
+        from sgl_kernel_npu.norm.split_qkv_rmsnorm_rope_pos_cache_half_npu import (
+            split_qkv_rmsnorm_rope_pos_cache_half_npu,
+        )
+    except (ImportError, OSError):
+        pass
+
 
 class LLaDA2MoeMLP(nn.Module):
     def __init__(
@@ -521,34 +530,59 @@ class LLaDA2MoeAttention(nn.Module):
         if hidden_states.shape[0] == 0:
             return hidden_states
         qkv, _ = self.query_key_value(hidden_states)
-        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
-        if self.use_qk_norm:
-            q, k = apply_qk_norm(
-                q=q,
-                k=k,
-                q_norm=self.query_layernorm,
-                k_norm=self.key_layernorm,
-                head_dim=self.head_dim,
-                alt_stream=self.alt_stream,
+        if _is_npu and split_qkv_rmsnorm_rope_pos_cache_half_npu is not None:
+            q, k, v = split_qkv_rmsnorm_rope_pos_cache_half_npu(
+                qkv,
+                positions,
+                self.rotary_emb.cos_sin_cache,
+                self.q_size,
+                self.kv_size,
+                self.head_dim,
+                eps=self.query_layernorm.variance_epsilon if self.use_qk_norm else None,
+                q_weight=self.query_layernorm.weight if self.use_qk_norm else None,
+                k_weight=self.key_layernorm.weight if self.use_qk_norm else None,
+                q_bias=(
+                    getattr(self.query_layernorm, "bias", None)
+                    if self.use_qk_norm
+                    else None
+                ),
+                k_bias=(
+                    getattr(self.key_layernorm, "bias", None)
+                    if self.use_qk_norm
+                    else None
+                ),
+                rope_dim=self.rotary_dim,
             )
-        can_fuse_set_kv = (
-            self.head_dim == self.rotary_emb.rotary_dim
-            and enable_fused_set_kv_buffer(forward_batch)
-        )
-        q, k = self.rotary_emb(
-            positions,
-            q,
-            k,
-            fused_set_kv_buffer_arg=(
-                create_fused_set_kv_buffer_arg(
-                    value=v,
-                    layer=self.attn,
-                    forward_batch=forward_batch,
+            can_fuse_set_kv = False
+        else:
+            q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+            if self.use_qk_norm:
+                q, k = apply_qk_norm(
+                    q=q,
+                    k=k,
+                    q_norm=self.query_layernorm,
+                    k_norm=self.key_layernorm,
+                    head_dim=self.head_dim,
+                    alt_stream=self.alt_stream,
                 )
-                if can_fuse_set_kv
-                else None
-            ),
-        )
+            can_fuse_set_kv = (
+                self.head_dim == self.rotary_emb.rotary_dim
+                and enable_fused_set_kv_buffer(forward_batch)
+            )
+            q, k = self.rotary_emb(
+                positions,
+                q,
+                k,
+                fused_set_kv_buffer_arg=(
+                    create_fused_set_kv_buffer_arg(
+                        value=v,
+                        layer=self.attn,
+                        forward_batch=forward_batch,
+                    )
+                    if can_fuse_set_kv
+                    else None
+                ),
+            )
         context_layer = self.attn(
             q,
             k,