diff --git a/mindspeed_llm/features_manager/__init__.py b/mindspeed_llm/features_manager/__init__.py
index 0fba65ab5da3d0882e5c8f56c0b5827816cfb022..01419ce684191239acdb4b6775b7d62580f3af70 100644
--- a/mindspeed_llm/features_manager/__init__.py
+++ b/mindspeed_llm/features_manager/__init__.py
@@ -13,6 +13,7 @@ from mindspeed.features_manager import (
     MoEAllGatherOverLapFeature,
     MoEFwdBwdOverlapFeature,
     MoEGmmFeature,
+    ExpertsPlacementFeature,
     MoEZeroMemoryFeature,
     OptimizeSendRecvCommFeature,
     SwapOptimizerFeature,
@@ -210,6 +211,7 @@ def add_moe_features(features_list: List[MindSpeedFeature]):
         MoEFwdBwdOverlapFeature(),
         MoEAlltoAllOverLapFeature(),
         MoEZeroMemoryFeature(),
+        ExpertsPlacementFeature(),
     ])
 
 
diff --git a/mindspeed_llm/training/training.py b/mindspeed_llm/training/training.py
index f2f55a599bc8e54630a68bc7903cf5c541ca8d70..ec7ab205fcf7345c26b1217f7dc2136c9d6af6bd 100644
--- a/mindspeed_llm/training/training.py
+++ b/mindspeed_llm/training/training.py
@@ -60,6 +60,11 @@ from megatron.core.distributed import DistributedDataParallel as DDP
 from megatron.core.distributed import finalize_model_grads
 from mindspeed_llm.training.initialize import set_jit_fusion_options
 from mindspeed_llm.tasks.posttrain.lora.utils import is_enable_lora
+from mindspeed.core.transformer.moe.expert_placement.planner import print_expert_load
+from mindspeed.core.transformer.moe.expert_placement.executor import (
+    build_param_params_module_mlp_map, 
+    expert_weight_and_optimizer_state_placement
+)
 
 # The earliest we can measure the start time.
 _TRAIN_START_TIME = time.time()
@@ -292,6 +297,14 @@ def build_train_args(*input_args):
             model_provider_func = model_provider
         model, optimizer, opt_param_scheduler = setup_model_and_optimizer(
             model_provider_func, model_type)
+        # param mapping to mlp object
+    if args.enable_expert_placement:
+        params_module_mlp_map = build_param_params_module_mlp_map(model)
+        if hasattr(optimizer, "chained_optimizers"):
+            for optimizer_sub in optimizer.chained_optimizers:
+                optimizer_sub.params_module_mlp_map = params_module_mlp_map
+        else:
+            optimizer.params_module_mlp_map = params_module_mlp_map 
     timers('model-and-optimizer-setup').stop()
     print_datetime('after model, optimizer, and learning rate '
                    'scheduler are built')
@@ -604,6 +617,11 @@ def train(forward_step_func, model, optimizer, opt_param_scheduler,
         update_num_microbatches(args.consumed_train_samples, consistency_check=True)
 
         args.curr_iteration = iteration
+        if args.enable_expert_placement:
+            expert_weight_and_optimizer_state_placement(args, model, optimizer)
+        if args.print_expert_load:
+            print_expert_load(args, model, iteration)
+            
         loss_dict, skipped_iter, should_checkpoint, should_exit, exit_code, grad_norm, num_zeros_in_grad = \
             train_step(forward_step_func,
                        train_data_iterator,