diff --git a/nemo/collections/nlp/models/language_modeling/megatron/gpt_layer_modelopt_spec.py b/nemo/collections/nlp/models/language_modeling/megatron/gpt_layer_modelopt_spec.py
index 080984133f3c..046e032093b1 100644
--- a/nemo/collections/nlp/models/language_modeling/megatron/gpt_layer_modelopt_spec.py
+++ b/nemo/collections/nlp/models/language_modeling/megatron/gpt_layer_modelopt_spec.py
@@ -20,7 +20,8 @@
     from megatron.core.transformer.enums import AttnMaskType
     from megatron.core.transformer.identity_op import IdentityOp
     from megatron.core.transformer.mlp import MLP, MLPSubmodules
-    from megatron.core.transformer.moe.moe_layer import MoELayer
+    from megatron.core.transformer.moe.moe_layer import MoELayer, MoESubmodules
+    from megatron.core.transformer.moe.shared_experts import SharedExpertMLP
     from megatron.core.transformer.spec_utils import ModuleSpec
     from megatron.core.transformer.transformer_layer import TransformerLayer, TransformerLayerSubmodules
 
@@ -48,6 +49,7 @@ def get_gpt_layer_modelopt_spec(num_experts: int = None) -> ModuleSpec:
     if not HAVE_MEGATRON_CORE:
         raise IMPORT_ERROR
 
+    mlp = _get_mlp_module_spec(num_experts=num_experts)
     return ModuleSpec(
         module=TransformerLayer,
         submodules=TransformerLayerSubmodules(
@@ -65,7 +67,7 @@ def get_gpt_layer_modelopt_spec(num_experts: int = None) -> ModuleSpec:
             ),
             self_attn_bda=get_bias_dropout_add,
             pre_mlp_layernorm=TENorm,
-            mlp=_get_mlp_module_spec(num_experts=num_experts),
+            mlp=mlp,
             mlp_bda=get_bias_dropout_add,
             # Map TE-layernorm-fusion keys back
             sharded_state_dict_keys_map={
@@ -77,7 +79,7 @@ def get_gpt_layer_modelopt_spec(num_experts: int = None) -> ModuleSpec:
 
 
 # Helper function to get module spec for MLP/MoE
-def _get_mlp_module_spec(num_experts: int = None, moe_grouped_gemm: bool = False) -> ModuleSpec:
+def _get_mlp_module_spec(num_experts: int = None) -> ModuleSpec:
     if num_experts is None:
         # Dense MLP w/ or w/o TE modules.
         return ModuleSpec(
@@ -91,12 +93,18 @@ def _get_mlp_module_spec(num_experts: int = None, moe_grouped_gemm: bool = False
         # Mixture of experts with modules in megatron core.
         return ModuleSpec(
             module=MoELayer,
-            submodules=(
-                MLPSubmodules(
+            submodules=MoESubmodules(
+                experts=MLPSubmodules(
                     linear_fc1=ColumnParallelLinear,
                     linear_fc2=RowParallelLinear,
-                )
-                if not moe_grouped_gemm
-                else None
+                ),
+                shared_experts=ModuleSpec(
+                    module=SharedExpertMLP,
+                    params={"gate": False},
+                    submodules=MLPSubmodules(
+                        linear_fc1=ColumnParallelLinear,
+                        linear_fc2=RowParallelLinear,
+                    ),
+                ),
             ),
         )