diff --git a/test/test_fused_rms_norm.py b/test/test_dtensor_rmsnorm.py
similarity index 63%
rename from test/test_fused_rms_norm.py
rename to test/test_dtensor_rmsnorm.py
index 9bd7e3732c..da08f42a3d 100644
--- a/test/test_fused_rms_norm.py
+++ b/test/test_dtensor_rmsnorm.py
@@ -12,6 +12,7 @@
     Shard,
 )
 from torch.distributed._tensor.debug import CommDebugMode
+from torch.distributed.tensor.parallel import parallelize_module, SequenceParallel
 from torch.testing._internal.common_utils import run_tests
 from torch.testing._internal.distributed._tensor.common_dtensor import (
     DTensorTestBase,
@@ -19,17 +20,17 @@
     with_comms,
 )
 
-from torchtitan.models.norms import fused_rms_norm_fn
+from torchtitan.models.norms import fused_rms_norm_fn, RMSNorm
 
 
-class TestFusedRMSNorm(DTensorTestBase):
+class TestDTensorRMSNorm(DTensorTestBase):
     @property
     def world_size(self):
         return 4
 
     @skip_if_lt_x_gpu(4)
     @with_comms
-    def test_fused_rms_norm(self):
+    def test_dtensor_fused_rmsnorm(self):
         mesh = init_device_mesh(
             device_type=self.device_type, mesh_shape=(self.world_size,)
         )
@@ -67,6 +68,34 @@ def test_fused_rms_norm(self):
         self.assertEqual(dist_out.full_tensor(), out)
         self.assertEqual(dist_grad_out.full_tensor(), grad_out)
 
+    @skip_if_lt_x_gpu(4)
+    @with_comms
+    def test_dtensor_compiled_rmsnorm(self):
+        mesh = init_device_mesh(
+            device_type=self.device_type, mesh_shape=(self.world_size,)
+        )
+        x = torch.randn(1, 4, 4, device=self.device_type)
+        dist_x = distribute_tensor(x, mesh, [Shard(1)])
+
+        x = x.clone().detach()
+        self.assertEqual(dist_x.full_tensor(), x)
+
+        dist_norm = RMSNorm(4, compile=True)
+        dist_norm.to(self.device_type)
+        norm = RMSNorm(4, compile=False)  # single-gpu eager as baseline
+        norm.to(self.device_type)
+
+        parallelize_module(dist_norm, mesh, SequenceParallel())
+
+        dist_out = dist_norm(dist_x)
+        out = norm(x)
+
+        dist_out.sum().backward()
+        out.sum().backward()
+
+        self.assertEqual(dist_out.full_tensor(), out)
+        self.assertEqual(dist_norm.weight.grad.full_tensor(), norm.weight.grad)
+
 
 if __name__ == "__main__":
     run_tests()