diff --git a/vllm/model_executor/layers/quantization/modelopt.py b/vllm/model_executor/layers/quantization/modelopt.py
index 37b682984fc35..f478cd319e667 100644
--- a/vllm/model_executor/layers/quantization/modelopt.py
+++ b/vllm/model_executor/layers/quantization/modelopt.py
@@ -37,6 +37,12 @@ from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig,
     QuantizeMethodBase,
 )
+from vllm.model_executor.layers.quantization.kernels.scaled_mm import (
+    init_fp8_linear_kernel,
+)
+from vllm.model_executor.layers.quantization.kernels.scaled_mm.ScaledMMLinearKernel import (  # noqa: E501
+    ScaledMMLinearQuantStrategy,
+)
 from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
 from vllm.model_executor.layers.quantization.utils.flashinfer_fp4_moe import (
     build_flashinfer_fp4_cutlass_moe_prepare_finalize,
@@ -68,7 +74,6 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
     swizzle_blockscale,
 )
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
-    Fp8LinearOp,
     requantize_with_max_scale,
 )
 from vllm.model_executor.parameter import ModelWeightParameter, PerTensorScaleParameter
@@ -254,8 +259,12 @@ class ModelOptFp8LinearMethod(LinearMethodBase):
 
     def __init__(self, quant_config: ModelOptFp8Config) -> None:
         self.quant_config = quant_config
-        self.fp8_linear = Fp8LinearOp(
-            act_quant_static=True, act_quant_group_shape=GroupShape.PER_TENSOR
+        self.fp8_linear = init_fp8_linear_kernel(
+            act_q_static=True,
+            act_q_group_shape=GroupShape.PER_TENSOR,
+            weight_quant_strategy=ScaledMMLinearQuantStrategy.TENSOR,
+            out_dtype=None,
+            module_name=self.__class__.__name__,
         )
 
     def create_weights(
@@ -323,13 +332,7 @@ class ModelOptFp8LinearMethod(LinearMethodBase):
         x: torch.Tensor,
         bias: torch.Tensor | None = None,
     ) -> torch.Tensor:
-        return self.fp8_linear.apply(
-            input=x,
-            weight=layer.weight,
-            weight_scale=layer.weight_scale,
-            input_scale=layer.input_scale,
-            bias=bias,
-        )
+        return self.fp8_linear.apply_weights(layer, x, bias)
 
 
 class ModelOptFp8MoEMethod(FusedMoEMethodBase):