diff --git a/test/neuron/run_tests.sh b/test/neuron/run_tests.sh
index f7671cc3d82..3472a073ea3 100755
--- a/test/neuron/run_tests.sh
+++ b/test/neuron/run_tests.sh
@@ -256,6 +256,7 @@ function run_xla_op_tests3 {
   #run_test "$_TEST_DIR/spmd/test_dtensor_integration2.py"
   run_test_multi_device "$_TEST_DIR/spmd/test_dtensor_convert_mesh.py"
   run_test_multi_device "$_TEST_DIR/spmd/test_xla_dtensor_spec_conv.py"
+  run_test_multi_device "$_TEST_DIR/spmd/test_xla_dtensor_from_local.py"
   run_test "$_TEST_DIR/spmd/test_xla_auto_sharding.py"
   #run_test "$_TEST_DIR/spmd/test_spmd_parameter_wrapping.py"
   run_test "$_TEST_DIR/spmd/test_train_spmd_linear_model.py"
diff --git a/test/run_tests.sh b/test/run_tests.sh
index b2cc8f751d2..dabcdb83961 100755
--- a/test/run_tests.sh
+++ b/test/run_tests.sh
@@ -255,6 +255,7 @@ function run_xla_op_tests3 {
   run_test_multi_devices_without_func "$_TEST_DIR/spmd/test_dtensor_integration3.py"
   run_test_multi_devices "$_TEST_DIR/spmd/test_dtensor_convert_mesh.py"
   run_test_multi_devices "$_TEST_DIR/spmd/test_xla_dtensor_spec_conversion.py"
+  run_test_multi_devices "$_TEST_DIR/spmd/test_xla_dtensor_from_local.py"
   run_test "$_TEST_DIR/spmd/test_xla_auto_sharding.py"
   run_test "$_TEST_DIR/spmd/test_spmd_parameter_wrapping.py"
   run_test "$_TEST_DIR/spmd/test_mp_input_sharding.py"
diff --git a/test/spmd/test_xla_dtensor_from_local.py b/test/spmd/test_xla_dtensor_from_local.py
new file mode 100644
index 00000000000..40647e20590
--- /dev/null
+++ b/test/spmd/test_xla_dtensor_from_local.py
@@ -0,0 +1,149 @@
+import sys
+import unittest
+import torch
+import numpy as np
+
+from torch.distributed.tensor import DeviceMesh
+from torch.distributed._tensor import DTensor
+from torch.distributed.tensor.placement_types import Replicate, Shard
+import torch_xla
+import torch_xla.runtime as xr
+import torch_xla.core.xla_model as xm
+from torch_xla.distributed.spmd.xla_sharded_tensor import XLAShardedTensor
+import test_xla_sharding_base
+
+
+class DTensorXLAFromLocalConversionTest(test_xla_sharding_base.XlaShardingTest):
+    """
+    Test suite for the automatic conversion of regular tensors to XLAShardedTensor
+    in DTensor.from_local() when using XLA device mesh.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+
+    def test_basic_conversion(self):
+        """Test basic conversion of regular tensor to XLAShardedTensor."""
+        world_size = xr.global_runtime_device_count()
+        
+        # Create a regular tensor (not on XLA device)
+        tensor = torch.randn(100_000, 88)
+        tensor_cpu = tensor.cpu()  # Keep a CPU copy for comparison
+        
+        # Create a DeviceMesh
+        device_mesh = DeviceMesh("xla", list(range(world_size)))
+        
+        # Use DTensor.from_local with the regular tensor
+        dt = DTensor.from_local(tensor, device_mesh=device_mesh)
+        
+        # Verify the tensor was converted correctly
+        self.assertEqual(dt.shape, tensor.shape)
+        
+        # Check the value of the tensor
+        torch.testing.assert_close(dt.global_tensor, tensor_cpu, check_device=False)
+        
+        # Verify operations work
+        result = dt + 1.0
+        self.assertEqual(result.shape, tensor.shape)
+        
+        print("Basic conversion successful")
+
+
+    def test_conversion_with_placements(self):
+        """Test conversion with explicit placements."""
+        world_size = xr.global_runtime_device_count()
+        
+        # Create a regular tensor (not on XLA device)
+        tensor = torch.randn(100_000, 88)
+        tensor_cpu = tensor.cpu()  # Keep a CPU copy for comparison
+        
+        # Create a DeviceMesh
+        device_mesh = DeviceMesh("xla", list(range(world_size)))
+        
+        # Use DTensor.from_local with explicit placements
+        dt = DTensor.from_local(
+            tensor,
+            device_mesh=device_mesh,
+            placements=[Replicate()]
+        )
+        
+        # Verify the tensor was converted correctly
+        self.assertEqual(dt.shape, tensor.shape)
+
+        # Check the value of the tensor
+        torch.testing.assert_close(dt.global_tensor, tensor_cpu, check_device=False)
+        
+        # Verify operations work
+        result = dt + 1.0
+        self.assertEqual(result.shape, tensor.shape)
+        
+        print("Conversion with placements successful")
+
+    def test_conversion_with_sharding(self):
+        """Test conversion with sharding placement."""
+        world_size = xr.global_runtime_device_count()
+        if world_size < 2:
+            self.skipTest("Need at least 2 devices for sharding test")
+        
+        # Create a tensor divisible by world_size
+        tensor = torch.randn(100_000, 88)
+        tensor_cpu = tensor.cpu()  # Keep a CPU copy for comparison
+        
+        # Create a DeviceMesh
+        device_mesh = DeviceMesh("xla", list(range(world_size)))
+        
+        # Use DTensor.from_local with sharding placement
+        dt = DTensor.from_local(
+            tensor,
+            device_mesh=device_mesh,
+            placements=[Shard(0)]
+        )
+        
+        # Verify the tensor was converted correctly
+        self.assertEqual(dt.shape, tensor.shape)
+
+        # Check the value of the tensor
+        torch.testing.assert_close(dt.global_tensor, tensor_cpu, check_device=False)
+        
+        # Verify operations work
+        result = dt + 1.0
+        self.assertEqual(result.shape, tensor.shape)
+        
+        print("Conversion with sharding successful")
+
+    def test_conversion_with_different_dtypes(self):
+        """Test conversion with different dtypes."""
+        world_size = xr.global_runtime_device_count()
+        device_mesh = DeviceMesh("xla", list(range(world_size)))
+        
+        # Test with different dtypes
+        for dtype in [torch.float16, torch.float32, torch.int32, torch.int64]:
+            # Create a tensor with specific dtype
+            tensor = torch.ones(100_000, 88, dtype=dtype)
+            tensor_cpu = tensor.cpu()  # Keep a CPU copy for comparison
+            
+            # Use DTensor.from_local with the tensor
+            dt = DTensor.from_local(tensor, device_mesh=device_mesh)
+            
+            # Verify dtype is preserved
+            self.assertEqual(dt.dtype, dtype)
+            
+            # Check the value of the tensor
+            torch.testing.assert_close(dt.global_tensor, tensor_cpu, check_device=False)
+            
+            # Verify operations work
+            if dtype.is_floating_point:
+                result = dt + 1.0
+            else:
+                result = dt + 1
+                
+            self.assertEqual(result.shape, tensor.shape)
+            self.assertEqual(result.dtype, dtype)
+            
+            print(f"Conversion with {dtype} successful")
+
+
+if __name__ == "__main__":
+    result = unittest.main(exit=False)
+    sys.exit(0 if result.result.wasSuccessful() else 1)
\ No newline at end of file
diff --git a/test/tpu/run_tests.sh b/test/tpu/run_tests.sh
index 24f18d3bdcd..ec585716cb6 100755
--- a/test/tpu/run_tests.sh
+++ b/test/tpu/run_tests.sh
@@ -62,6 +62,7 @@ run_test "$_TEST_DIR/spmd/test_xla_auto_sharding.py"
 run_test "$_TEST_DIR/spmd/test_fsdp_v2.py"
 run_test "$_TEST_DIR/spmd/test_dtensor_convert_mesh.py"
 run_test "$_TEST_DIR/spmd/test_xla_dtensor_spec_conversion.py"
+run_test "$_TEST_DIR/spmd/test_xla_dtensor_from_local.py"
 run_test "$_TEST_DIR/test_gradient_accumulation.py"
 XLA_EXPERIMENTAL=nonzero:masked_select:nms run_test "$_TEST_DIR/ds/test_dynamic_shape_models.py" -v
 run_test "$_TEST_DIR/test_autocast.py"