diff --git a/colossalai/booster/plugin/low_level_zero_plugin.py b/colossalai/booster/plugin/low_level_zero_plugin.py
index 64f264f7e..63d46f6f8 100644
--- a/colossalai/booster/plugin/low_level_zero_plugin.py
+++ b/colossalai/booster/plugin/low_level_zero_plugin.py
@@ -35,6 +35,7 @@ from colossalai.interface import AMPModelMixin, ModelWrapper, OptimizerWrapper
 from colossalai.interface.optimizer import DistributedOptim
 from colossalai.nn.optimizer import DistGaloreAwamW, cast_to_distributed
 from colossalai.quantization import BnbQuantizationConfig, quantize_model
+from colossalai.quantization.fp8_hook import FP8Hook
 from colossalai.tensor.colo_parameter import ColoParameter
 from colossalai.tensor.param_op_hook import ColoParamOpHookManager
 from colossalai.zero import LowLevelZeroOptimizer
@@ -62,7 +63,9 @@ class OptimizerParamCheckState(enum.Enum):
 
 
 class LowLevelZeroModel(ModelWrapper, AMPModelMixin):
-    def __init__(self, module: nn.Module, precision: str, overlap_allgather: bool = False) -> None:
+    def __init__(
+        self, module: nn.Module, precision: str, overlap_allgather: bool = False, use_fp8: bool = False
+    ) -> None:
         super().__init__(module)
         self.dtype = None
         if precision == "fp16":
@@ -74,11 +77,16 @@ class LowLevelZeroModel(ModelWrapper, AMPModelMixin):
         module = module.to(get_accelerator().get_current_device())
         self.module = module
         self.convert_fn = None
+        self.use_fp8 = use_fp8
         if self.dtype is not None:
             self.convert_fn = partial(_convert_floating_point, dtype=self.dtype)
         self.overlap_allgather = overlap_allgather
+        self.op_hooks = []
         if overlap_allgather:
-            self.op_hook = ZeroOpHook()
+            self.op_hooks.append(ZeroOpHook())
+        if use_fp8:
+            self.op_hooks.append(FP8Hook())
+        if overlap_allgather or use_fp8:
             for p in module.parameters():
                 if p.requires_grad and type(p) is not ColoParameter:
                     p.__class__ = ColoParameter
@@ -335,6 +343,7 @@ class LowLevelZeroPlugin(DPPluginBase):
         master_weights: bool = True,
         verbose: bool = False,
         fp8_communication: bool = False,
+        use_fp8: bool = False,
     ) -> None:
         super().__init__()
         assert stage in (1, 2), f"LowLevelZeroPlugin only supports stage 1/2 training"
@@ -362,6 +371,7 @@ class LowLevelZeroPlugin(DPPluginBase):
         )
         self.lora_enabled = False
         self.verbose = verbose
+        self.use_fp8 = use_fp8
 
         # set class name with stage, for better error message
         setattr(self.__class__, "__name__", f"LowLevelZeroPlugin_ZeRO-{stage}")
@@ -476,7 +486,10 @@ class LowLevelZeroPlugin(DPPluginBase):
 
         if not isinstance(model, ModelWrapper):
             model = LowLevelZeroModel(
-                model, self.precision, overlap_allgather=self.zero_optim_kwargs["overlap_allgather"]
+                model,
+                self.precision,
+                overlap_allgather=self.zero_optim_kwargs["overlap_allgather"],
+                use_fp8=self.use_fp8,
             )
 
         # TODO: Support Galore + ZeRO
diff --git a/examples/language/llama/benchmark.py b/examples/language/llama/benchmark.py
index 07583161b..21d081145 100644
--- a/examples/language/llama/benchmark.py
+++ b/examples/language/llama/benchmark.py
@@ -259,7 +259,6 @@ def main():
         if isinstance(plugin, (GeminiPlugin, HybridParallelPlugin))
         else nullcontext()
     )
-
     init_kwargs = {}
     if config.model_type == "chatglm":
         init_kwargs["empty_init"] = False