diff --git a/CHANGELOG.md b/CHANGELOG.md
index 3da34016f9ec0..985c27195a266 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -239,6 +239,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Removed `HorovodPlugin.start_{training,evaluating,predicting}` hooks ([#10989](https://github.com/PyTorchLightning/pytorch-lightning/pull/10989))
 
 
+- Removed `Accelerator.on_train_start` ([#10999](https://github.com/PyTorchLightning/pytorch-lightning/pull/10999))
 
 ### Fixed
 
diff --git a/pytorch_lightning/accelerators/accelerator.py b/pytorch_lightning/accelerators/accelerator.py
index 69a5bc1091c28..18fd855c94a60 100644
--- a/pytorch_lightning/accelerators/accelerator.py
+++ b/pytorch_lightning/accelerators/accelerator.py
@@ -113,10 +113,6 @@ def get_device_stats(self, device: Union[str, torch.device]) -> Dict[str, Any]:
         """
         raise NotImplementedError
 
-    def on_train_start(self) -> None:
-        """Called when train begins."""
-        return self.training_type_plugin.on_train_start()
-
     @staticmethod
     @abstractmethod
     def auto_device_count() -> int:
diff --git a/pytorch_lightning/accelerators/gpu.py b/pytorch_lightning/accelerators/gpu.py
index 49d0770e54ff0..c6c82d83c32f5 100644
--- a/pytorch_lightning/accelerators/gpu.py
+++ b/pytorch_lightning/accelerators/gpu.py
@@ -45,10 +45,7 @@ def setup_environment(self) -> None:
 
     def setup(self, trainer: "pl.Trainer") -> None:
         self.set_nvidia_flags(trainer.local_rank)
-        return super().setup(trainer)
-
-    def on_train_start(self) -> None:
-        super().on_train_start()
+        super().setup(trainer)
         # clear cache before training
         torch.cuda.empty_cache()
 
diff --git a/pytorch_lightning/loops/fit_loop.py b/pytorch_lightning/loops/fit_loop.py
index f15b0b954361a..0bcd10f916527 100644
--- a/pytorch_lightning/loops/fit_loop.py
+++ b/pytorch_lightning/loops/fit_loop.py
@@ -195,7 +195,7 @@ def on_run_start(self) -> None:  # type: ignore[override]
         self._results.to(device=self.trainer.lightning_module.device)
         self.trainer._call_callback_hooks("on_train_start")
         self.trainer._call_lightning_module_hook("on_train_start")
-        self.trainer._call_accelerator_hook("on_train_start")
+        self.trainer._call_ttp_hook("on_train_start")
 
     def on_advance_start(self) -> None:  # type: ignore[override]
         """Prepares the dataloader for training and calls the hooks ``on_epoch_start`` and
diff --git a/pytorch_lightning/trainer/trainer.py b/pytorch_lightning/trainer/trainer.py
index d6a5910a630db..dde8edce21b77 100644
--- a/pytorch_lightning/trainer/trainer.py
+++ b/pytorch_lightning/trainer/trainer.py
@@ -1592,29 +1592,6 @@ def _call_ttp_hook(
 
         return output
 
-    # TODO: eventually no longer need this
-    def _call_accelerator_hook(
-        self,
-        hook_name: str,
-        *args: Any,
-        **kwargs: Any,
-    ) -> Any:
-        pl_module = self.lightning_module
-        prev_fx_name = pl_module._current_fx_name
-        pl_module._current_fx_name = hook_name
-
-        fn = getattr(self.accelerator, hook_name)
-        if not callable(fn):
-            return
-
-        with self.profiler.profile(hook_name):
-            output = fn(*args, **kwargs)
-
-        # restore current_fx when nested context
-        pl_module._current_fx_name = prev_fx_name
-
-        return output
-
     @staticmethod
     def _parse_devices(
         gpus: Optional[Union[List[int], str, int]],