diff --git a/dolomite_engine/checkpointing.py b/dolomite_engine/checkpointing.py
index 9462e08..f5dae98 100644
--- a/dolomite_engine/checkpointing.py
+++ b/dolomite_engine/checkpointing.py
@@ -73,6 +73,28 @@ def load_state_dict(self, state_dict: dict) -> None:
             set_model_state_dict(model, state_dict[i], options=StateDictOptions(strict=not has_teacher_model))
 
 
+class _OptimizerSaver(Stateful):
+    def __init__(self, model_container: ModelContainer, optimizer_container: OptimizerContainer) -> None:
+        self.model_container = model_container
+        self.optimizer_container = optimizer_container
+
+    def state_dict(self):
+        if self.optimizer_container is None:
+            return []
+
+        return [
+            get_optimizer_state_dict(model, optimizer)
+            for model, optimizer in zip(self.model_container, self.optimizer_container)
+        ]
+
+    def load_state_dict(self, state_dict: dict) -> None:
+        if self.optimizer_container is None:
+            return
+
+        for i, (model, optimizer) in enumerate(zip(self.model_container, self.optimizer_container)):
+            set_optimizer_state_dict(model, optimizer, optim_state_dict=state_dict[i])
+
+
 def save_checkpoint(
     args: TrainingArgs,
     model_container: ModelContainer,
@@ -104,50 +126,51 @@ def save_checkpoint(
     save_path = _get_base_path(args.save_args.save_path, iteration)
     os.makedirs(save_path, exist_ok=True)
 
-    model_saver = _ModelSaver(model_container)
+    savers = {"model": _ModelSaver(model_container)}
 
-    if optimizer_container is None:
-        optimizer_container = [None] * len(model_container)
-
-    if lr_scheduler_container is None:
-        lr_scheduler_container = [None] * len(model_container)
-
-    assert len(model_container) == len(optimizer_container)
-    assert len(model_container) == len(lr_scheduler_container)
+    if save_optimizer:
+        if optimizer_container is None:
+            log_rank_0(
+                logging.WARN,
+                "optimizer_container is not passed to save_checkpoint but save_optimizer is set to True. "
+                "Therefore, the function will not save the optimizer",
+            )
+        else:
+            savers["optimizer"] = _OptimizerSaver(model_container, optimizer_container)
 
     _, pipeline_stage_ids_on_current_rank = get_pipeline_num_stages_and_stage_ids_on_current_rank(
         args.distributed_args.num_pipeline_stages
     )
 
-    dcp.save(model_saver, checkpoint_id=_get_model_path(save_path))
-
-    for pipeline_stage, model, optimizer, lr_scheduler in zip(
-        pipeline_stage_ids_on_current_rank, model_container, optimizer_container, lr_scheduler_container
-    ):
-        if save_optimizer:
-            if optimizer is None:
-                log_rank_0(
-                    logging.WARN,
-                    "optimizer_container is not passed to save_checkpoint but save_optimizer is set to True. "
-                    "Therefore, the function will not save the optimizer",
-                )
-            else:
-                # TODO add options=StateDictOptions(flatten_optimizer_state_dict=True))
-                dcp.save(
-                    get_optimizer_state_dict(model, optimizer),
-                    checkpoint_id=_get_optimizer_path(save_path, pipeline_stage=pipeline_stage),
-                )
-
-        if lr_scheduler is None:
-            log_rank_0(
-                logging.WARN,
-                "lr_scheduler is not passed to save_checkpoint. Therefore, the function will not save the lr_scheduler",
-            )
-        else:
-            run_rank_n(torch.save)(
-                lr_scheduler.state_dict(),
-                _get_lr_scheduler_path(save_path, pipeline_stage=pipeline_stage),
-            )
+    dcp.save(savers, checkpoint_id=_get_model_path(save_path))
+
+    # for pipeline_stage, model, optimizer, lr_scheduler in zip(
+    #     pipeline_stage_ids_on_current_rank, model_container, optimizer_container, lr_scheduler_container
+    # ):
+    #     if save_optimizer:
+    #         if optimizer is None:
+    #             log_rank_0(
+    #                 logging.WARN,
+    #                 "optimizer_container is not passed to save_checkpoint but save_optimizer is set to True. "
+    #                 "Therefore, the function will not save the optimizer",
+    #             )
+    #         else:
+    #             # TODO add options=StateDictOptions(flatten_optimizer_state_dict=True))
+    #             dcp.save(
+    #                 get_optimizer_state_dict(model, optimizer),
+    #                 checkpoint_id=_get_optimizer_path(save_path, pipeline_stage=pipeline_stage),
+    #             )
+
+    #     if lr_scheduler is None:
+    #         log_rank_0(
+    #             logging.WARN,
+    #             "lr_scheduler is not passed to save_checkpoint. Therefore, the function will not save the lr_scheduler",
+    #         )
+    #     else:
+    #         run_rank_n(torch.save)(
+    #             lr_scheduler.state_dict(),
+    #             _get_lr_scheduler_path(save_path, pipeline_stage=pipeline_stage),
+    #         )
 
     rng_state = {
         "random_rng_state": random.getstate(),