diff --git a/test/test_low_bit_optim.py b/test/test_low_bit_optim.py
index b0edfc7fc5..b6a0f54662 100644
--- a/test/test_low_bit_optim.py
+++ b/test/test_low_bit_optim.py
@@ -119,6 +119,45 @@ def test_bf16_stochastic_round(self, device, compile):
         # must cast BF16 tensor back to FP32 so that .mean() is accurate
         torch.testing.assert_close(x_rep_bf16.float().mean(1), x, atol=3e-5, rtol=3e-5)
 
+    @parametrize("device", _DEVICES)
+    @parametrize("compile", [False, True])
+    def test_bf16_stochastic_round_dtensor(self, device, compile):
+        pytest.importorskip("torch.distributed")
+        import torch.distributed as dist
+        from torch.distributed.tensor import DTensor, Replicate
+        from torch.distributed.device_mesh import init_device_mesh
+
+        created_pg = False
+        if dist.is_available() and not dist.is_initialized():
+            store = dist.TCPStore("127.0.0.1", 29500, 1, True)
+            dist.init_process_group(
+                backend="gloo",
+                store=store,
+                rank=0,
+                world_size=1,
+            )
+            created_pg = True
+
+        try:
+            torch.manual_seed(common_utils.SEED)
+            x = torch.rand(32, device=device) * 100
+            x_rep = x.view(-1, 1).repeat(1, 100_000)
+
+            func = torch.compile(
+                _fp32_to_bf16_sr, fullgraph=True, dynamic=False, disable=not compile
+            )
+            out_plain = func(x_rep)
+
+            mesh = init_device_mesh(device, (1,))
+            x_dt = DTensor.from_local(x_rep, mesh, [Replicate()], run_check=False)
+            out_dt = func(x_dt)
+
+            assert isinstance(out_dt, DTensor)
+            torch.testing.assert_close(out_dt.to_local(), out_plain)
+        finally:
+            if created_pg:
+                dist.destroy_process_group()
+
 
 class TestOptim(TestCase):
     @parametrize(
diff --git a/torchao/optim/quant_utils.py b/torchao/optim/quant_utils.py
index a4035fde1c..bef297545d 100644
--- a/torchao/optim/quant_utils.py
+++ b/torchao/optim/quant_utils.py
@@ -5,6 +5,7 @@
 # LICENSE file in the root directory of this source tree.
 import torch
 from torch import Tensor
+from torch.distributed.tensor import DTensor
 
 
 # https://github.com/TimDettmers/bitsandbytes/blob/dada530149212d64d4b69534716202659ef37ec8/bitsandbytes/functional.py#L339-L391
@@ -117,7 +118,7 @@ def dequant_with_qmap(codes: Tensor, qmap: Tensor, scale: Tensor):
     return out.view(codes.shape)
 
 
-def _fp32_to_bf16_sr(x_f32: Tensor) -> Tensor:
+def _fp32_to_bf16_sr(_x_f32: Tensor) -> Tensor:
     # For an FP32 number      [a31, ..., a16, a15, ..., a0] to be converted to BF16
     # - Round towards zero:   [a31, ..., a16,   0, ...,  0]
     # - Round away from zero: [a31, ..., a16+1, 0, ...,  0]
@@ -127,6 +128,9 @@ def _fp32_to_bf16_sr(x_f32: Tensor) -> Tensor:
     # [a15, ..., a0] / 2^16, where the bit pattern [a15, ..., a0] is interpreted as uint16
     #
     # we have to use int32 since most arithmetic ops are not implemented for uint32/int16/uint16
+    is_dt = isinstance(_x_f32, DTensor)
+    x_f32 = _x_f32.to_local() if is_dt else _x_f32
+
     rand_16bit = torch.randint(
         0, 1 << 16, x_f32.shape, device=x_f32.device, dtype=torch.int32
     )
@@ -142,4 +146,9 @@ def _fp32_to_bf16_sr(x_f32: Tensor) -> Tensor:
     )
     # alternative, slightly faster
     # x_f32_bits = (x_f32_bits + rand_16bit) & 0xFFFF0000
-    return x_f32_bits.view(torch.float32).bfloat16()
+    x_bf16_trunc = x_f32_bits.view(torch.float32).bfloat16()
+
+    return DTensor.from_local(
+        x_bf16_trunc, _x_f32.device_mesh, _x_f32.placements,
+        run_check=False, shape=tuple(_x_f32.shape), stride=tuple(_x_f32.stride()),
+    ) if is_dt else x_bf16_trunc