diff --git a/tutorials/developer_api_guide/tensor_parallel.py b/tutorials/developer_api_guide/tensor_parallel.py
index 87c94b896a..69539d1e21 100644
--- a/tutorials/developer_api_guide/tensor_parallel.py
+++ b/tutorials/developer_api_guide/tensor_parallel.py
@@ -41,32 +41,47 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 # Test #
 ########
 if __name__ == "__main__":
+    # To make sure different ranks create the same module
+    torch.manual_seed(5)
+
     m = M()
-    example_inputs = (100 * torch.randn(128, 1024),)
-    m(*example_inputs)
+    example_input = 100 * torch.randn(128, 1024)
+    m(example_input)
 
 
     import os
-    import torch
-    from torch.distributed._tensor import init_device_mesh, Shard, distribute_tensor
+    from torch.distributed._tensor import DTensor, Replicate, Shard
     import torch.distributed as dist
+
     # initialize a fake process group
-    store = torch.testing._internal.distributed.fake_pg.FakeStore()
-    dist.init_process_group(
-        backend="fake",
-        world_size=2,
-        rank=0,
-        store=store,
-    )
-    mesh = init_device_mesh("cuda", (int(os.environ["WORLD_SIZE"]),))
+    world_size = int(os.environ["WORLD_SIZE"])
+    rank = int(os.environ["RANK"])
+    dist.init_process_group(backend="nccl")
+    mesh = dist.init_device_mesh("cuda", (world_size,))
+
     # Shard this tensor over the mesh by sharding `big_tensor`'s 0th dimension over the 0th dimension of `mesh`.
-    quantized_weight = to_my_dtype_tp(m.linear.weight)
+    orig_weight = m.linear.weight
+    quantized_weight = to_my_dtype_tp(orig_weight)
     print("quantized weight:", quantized_weight)
-    quantized_weight_dtensor = distribute_tensor(quantized_weight, mesh, [Shard(dim=0)])
-    print("quantized weight dtensor:", quantized_weight_dtensor)
+    # Number of rows per rank
+    n_local_rows = orig_weight.size(0) // world_size
+    # TODO: add support for aten.slice.Tensor
+    quantized_shard = quantized_weight[rank * n_local_rows : (rank + 1) * n_local_rows, :]
+    print("quantized shard:", quantized_shard)
+    # Construct DTensor from local shard
+    quantized_dtensor = DTensor.from_local(quantized_shard, device_mesh, [Shard(0)])
+    print("quantized dtensor:", quantized_dtensor)
 
+    # Replace parameter in module
     m.linear.weight = torch.nn.Parameter(
-        quantized_weight_dtensor, requires_grad=False
+        quantized_dtensor, requires_grad=False
     )
 
-    m(*example_inputs)
+    # We need to turn inputs into DTensor form as well -- just a format change
+    input_dtensor = DTensor.from_local(
+        example_input, mesh, [Replicate()]
+    )
+    print("input dtensor:", input_dtensor)
+
+    m(input_dtensor)
+