diff --git a/src/mcore_bridge/model/modules/gated_delta_net.py b/src/mcore_bridge/model/modules/gated_delta_net.py
index 93b129a..87580fd 100644
--- a/src/mcore_bridge/model/modules/gated_delta_net.py
+++ b/src/mcore_bridge/model/modules/gated_delta_net.py
@@ -3,6 +3,7 @@
 import torch.nn.functional as F
 from megatron.core.inference.contexts import BaseInferenceContext
 from megatron.core.packed_seq_params import PackedSeqParams
+from megatron.core.transformer.utils import make_sharded_tensors_for_checkpoint, sharded_state_dict_default
 from typing import List, Optional
 
 try:
@@ -312,3 +313,49 @@ def forward(
         nvtx_range_pop(suffix='out_proj')
 
         return out, out_bias
+
+    def sharded_state_dict(self, prefix='', sharded_offsets=(), metadata=None, tp_group=None):
+        """Provide a sharded state dictionary for distributed checkpointing."""
+        from megatron.core.transformer.utils import ensure_metadata_has_dp_cp_group
+
+        # Guard for cases metadata is not provided
+        metadata = ensure_metadata_has_dp_cp_group(metadata)
+
+        sharded_state_dict = {}
+        # Parameters
+        self._save_to_state_dict(sharded_state_dict, '', keep_vars=True)
+        sharded_state_dict = make_sharded_tensors_for_checkpoint(
+            sharded_state_dict,
+            prefix,
+            tensor_parallel_layers_axis_map={
+                'A_log': 0,
+                'dt_bias': 0,
+            },  # parameters sharded across TP
+            sharded_offsets=sharded_offsets,
+            tp_group=(tp_group if tp_group is not None else self.pg_collection.tp),
+            dp_cp_group=metadata['dp_cp_group'],
+        )
+        # Submodules
+        tp_group = tp_group if tp_group is not None else self.pg_collection.tp
+        for name, module in self.named_children():
+            if name == 'conv1d':
+                # Add TP sharding for Conv1d
+                module_sd = module.state_dict(prefix='', keep_vars=True)
+                tp_sharding_map = {'weight': 0}
+                if self.conv_bias:
+                    tp_sharding_map['bias'] = 0
+                module_sharded_sd = make_sharded_tensors_for_checkpoint(
+                    module_sd,
+                    f'{prefix}{name}.',
+                    tp_sharding_map,
+                    sharded_offsets,
+                    tp_group=tp_group,
+                    dp_cp_group=metadata['dp_cp_group'],
+                )
+            else:
+                module_sharded_sd = sharded_state_dict_default(
+                    module, f'{prefix}{name}.', sharded_offsets, metadata, tp_group=tp_group)
+
+            sharded_state_dict.update(module_sharded_sd)
+
+        return sharded_state_dict