diff --git a/TrainingExtensions/torch/src/python/aimet_torch/elementwise_ops.py b/TrainingExtensions/torch/src/python/aimet_torch/elementwise_ops.py
index 4445ab02244..65b4ff943dc 100644
--- a/TrainingExtensions/torch/src/python/aimet_torch/elementwise_ops.py
+++ b/TrainingExtensions/torch/src/python/aimet_torch/elementwise_ops.py
@@ -420,6 +420,9 @@ def forward(self, data: torch.Tensor, indices: torch.Tensor) -> torch.Tensor:
         """
         Forward-pass routine for GatherNd op
         """
+        if self.batch_dims == 0:
+            return self._gather_nd(data, indices)
+
         data_rank = len(data.shape)
 
         assert indices.shape[-1] <= data_rank
@@ -455,6 +458,29 @@ def forward(self, data: torch.Tensor, indices: torch.Tensor) -> torch.Tensor:
             return torch.tensor(output_data_buffer, device=data.device).reshape(output_shape)
         return torch.cat(output_data_buffer).reshape(output_shape)
 
+    @staticmethod
+    def _gather_nd(data: torch.Tensor, indices: torch.Tensor) -> torch.Tensor:
+        """
+        GatherNd operation for batch_dim=0 case
+
+        :param data: Tensor to gather values
+        :param indices: Index tensor to be used to gather values
+        :return: Tensor after GatherNd operation
+        """
+        data_rank, m = len(data.shape), indices.shape[-1]
+        assert (
+            m <= data_rank
+        ), f"m: {m} should be less than or equal to data_rank: {data_rank}"
+
+        total_samples = indices.shape[:-1].numel()
+        output_shape = indices.shape[:-1] + data.shape[m:]
+        reshaped_indices = torch.split(
+            tensor=indices.reshape(total_samples, m).transpose(0, 1),
+            split_size_or_sections=1,
+        )
+
+        return data[reshaped_indices].reshape(output_shape).contiguous()
+
 
 class ScatterElements(torch.nn.Module):
     """ ScatterElements op implementation """