diff --git a/tests/e2e/singlecard/compile/test_norm_quant_fusion.py b/tests/e2e/singlecard/compile/test_norm_quant_fusion.py
index 057fe888..d08e69c4 100644
--- a/tests/e2e/singlecard/compile/test_norm_quant_fusion.py
+++ b/tests/e2e/singlecard/compile/test_norm_quant_fusion.py
@@ -32,6 +32,7 @@ from tests.e2e.singlecard.compile.backend import TestBackend
 from vllm_ascend.ascend_forward_context import set_ascend_forward_context
 from vllm_ascend.compilation.passes.norm_quant_fusion_pass import \
     AddRMSNormQuantFusionPass
+from vllm_ascend.utils import enable_custom_op
 
 
 class TestModelWithoutBias(nn.Module):
@@ -124,11 +125,8 @@ class TestModelWithBias(nn.Module):
         """
         residual = torch.zeros_like(x)
 
-        norm_output, _, new_residual = torch_npu.npu_add_rms_norm(
-            x, residual, self.rms_norm_weight, self.eps)
-
-        # Add bias
-        norm_output_with_bias = norm_output + self.bias
+        norm_output_with_bias, _, new_residual = torch.ops._C_ascend.npu_add_rms_norm_bias(
+            x, residual, self.rms_norm_weight, self.bias, self.eps)
 
         quantized_output = torch.ops.vllm.quantize(norm_output_with_bias,
                                                    self.quant_scale,
@@ -140,8 +138,7 @@ class TestModelWithBias(nn.Module):
     def ops_in_model_before(self) -> List[OpOverload]:
         """Return the list of expected operators BEFORE fusion."""
         return [
-            torch.ops.npu.npu_add_rms_norm.default,
-            torch.ops.aten.add.Tensor,  # Add bias operation
+            torch.ops._C_ascend.npu_add_rms_norm_bias.default,
             torch.ops.vllm.quantize.default
         ]
 
@@ -249,11 +246,8 @@ class TestModelSPWithBias(nn.Module):
         """
         residual = torch.zeros_like(x)
 
-        norm_output, _, new_residual = torch_npu.npu_add_rms_norm(
-            x, residual, self.rms_norm_weight, self.eps)
-
-        # Add bias
-        norm_output_with_bias = norm_output + self.bias
+        norm_output_with_bias, _, new_residual = torch.ops._C_ascend.npu_add_rms_norm_bias(
+            x, residual, self.rms_norm_weight, self.bias, self.eps)
 
         norm_output_with_bias = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
             norm_output_with_bias, True)
@@ -268,8 +262,7 @@ class TestModelSPWithBias(nn.Module):
     def ops_in_model_before(self) -> List[OpOverload]:
         """Return the list of expected operators BEFORE fusion."""
         return [
-            torch.ops.npu.npu_add_rms_norm.default,
-            torch.ops.aten.add.Tensor,  # Add bias operation
+            torch.ops._C_ascend.npu_add_rms_norm_bias.default,
             torch.ops.vllm.maybe_all_gather_and_maybe_unpad.default,
             torch.ops.vllm.quantize.default
         ]
@@ -322,6 +315,8 @@ def test_rmsnorm_quant_fusion(
                 AddRMSNormQuantFusionPass(vllm_config=vllm_config)
             ])
             if use_bias:
+                if not enable_custom_op():
+                    return
                 if sp_enable:
                     model = TestModelSPWithBias(hidden_size,
                                                 dtype,
diff --git a/vllm_ascend/compilation/passes/norm_quant_fusion_pass.py b/vllm_ascend/compilation/passes/norm_quant_fusion_pass.py
index e26b8429..9bea5ca5 100644
--- a/vllm_ascend/compilation/passes/norm_quant_fusion_pass.py
+++ b/vllm_ascend/compilation/passes/norm_quant_fusion_pass.py
@@ -23,6 +23,8 @@ from vllm.config import VllmConfig
 from vllm.config.compilation import Range
 from vllm.logger import logger
 
+from vllm_ascend.utils import enable_custom_op
+
 
 class AddRMSNormQuantPattern:
     def __init__(self, vllm_config: VllmConfig, eps: float = 1e-6):
@@ -113,10 +115,11 @@ class AddRMSNormQuantPatternWithBias:
             """
             Pattern for AddRMSNormQuant fusion.
             """
-            output = torch.ops.npu.npu_add_rms_norm(rms_norm_input, residual, rms_norm_weight, self.eps)
+            output = torch.ops._C_ascend.npu_add_rms_norm_bias(
+                rms_norm_input, residual, rms_norm_weight, bias, self.eps
+            )
             out0 = output[0]
             out1 = output[2]
-            out0 = out0 + bias
             quantized_output = torch.ops.vllm.quantize(out0, scale, scale_reciprocal, offset)
             return quantized_output, out1
 
@@ -233,10 +236,11 @@ class AddRMSNormQuantSPPatternWithBias:
             """
             Pattern for AddRMSNormQuant fusion.
             """
-            output = torch.ops.npu.npu_add_rms_norm(rms_norm_input, residual, rms_norm_weight, self.eps)
+            output = torch.ops._C_ascend.npu_add_rms_norm_bias(
+                rms_norm_input, residual, rms_norm_weight, bias, self.eps
+            )
             out0 = output[0]
             out1 = output[2]
-            out0 = out0 + bias
             out0 = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(out0, True)
             quantized_output = torch.ops.vllm.quantize(out0, scale, scale_reciprocal, offset)
             return quantized_output, out1
@@ -281,9 +285,10 @@ class AddRMSNormQuantFusionPass(VllmInductorPass):
         common_epsilons = [1e-5, 1e-6]
         for eps in common_epsilons:
             AddRMSNormQuantPattern(vllm_config, eps=eps).register(self.pattern_match_passes)
-            AddRMSNormQuantPatternWithBias(vllm_config, eps=eps).register(self.pattern_match_passes)
             AddRMSNormQuantSPPattern(vllm_config, eps=eps).register(self.pattern_match_passes)
-            AddRMSNormQuantSPPatternWithBias(vllm_config, eps=eps).register(self.pattern_match_passes)
+            if enable_custom_op():
+                AddRMSNormQuantPatternWithBias(vllm_config, eps=eps).register(self.pattern_match_passes)
+                AddRMSNormQuantSPPatternWithBias(vllm_config, eps=eps).register(self.pattern_match_passes)
 
     def __call__(self, graph: torch.fx.Graph):
         self.begin()