diff --git a/colossalai/trainer/hooks/_mem_tracer_hook.py b/colossalai/trainer/hooks/_mem_tracer_hook.py
deleted file mode 100644
index 29c5d9b3c..000000000
--- a/colossalai/trainer/hooks/_mem_tracer_hook.py
+++ /dev/null
@@ -1,44 +0,0 @@
-from colossalai.registry import HOOKS
-from torch import Tensor
-from colossalai.trainer.hooks import BaseHook
-from colossalai.gemini.memory_tracer import AsyncMemoryMonitor
-
-
-@HOOKS.register_module
-class MemTraceHook(BaseHook):
-    """Save memory stats and pass it to states
-    This hook is used to record memory usage info, and pass to trainer.states
-    You can use it as other trainer hook and fetch data from trainer.states['metrics][mode]
-    """
-
-    def __init__(
-        self,
-        priority: int = 0,
-    ) -> None:
-        super().__init__(priority=priority)
-        self._memory_monitor = AsyncMemoryMonitor()
-
-    def after_hook_is_attached(self, trainer):
-        # Initialize the data
-        trainer.states['metrics']['train'] = self._memory_monitor.state_dict
-        trainer.states['metrics']['test'] = self._memory_monitor.state_dict
-
-    def before_train_iter(self, trainer):
-        self._memory_monitor.start()
-        return super().before_train_iter(trainer)
-
-    def after_train_iter(self, trainer, output: Tensor, label: Tensor, loss: Tensor):
-        self._memory_monitor.finish()
-        trainer.states['metrics']['train'] = self._memory_monitor.state_dict
-        trainer.states['metrics']['test'] = self._memory_monitor.state_dict
-        return super().after_train_iter(trainer, output, label, loss)
-
-    def before_test_iter(self, trainer):
-        self._memory_monitor.start()
-        return super().before_test(trainer)
-
-    def after_test_iter(self, trainer, output: Tensor, label: Tensor, loss: Tensor):
-        self._memory_monitor.finish()
-        trainer.states['metrics']['train'] = self._memory_monitor.state_dict
-        trainer.states['metrics']['test'] = self._memory_monitor.state_dict
-        return super().after_test_iter(trainer, output, label, loss)