diff --git a/test/null/test_tensor_uop_mixin.py b/test/null/test_tensor_uop_mixin.py
index 906f960843..6debed4277 100644
--- a/test/null/test_tensor_uop_mixin.py
+++ b/test/null/test_tensor_uop_mixin.py
@@ -2,7 +2,10 @@ import math, unittest
 from tinygrad import Tensor, dtypes
 from tinygrad.uop.ops import UOp, UPat, Ops, PatternMatcher, graph_rewrite
 
-_strip_unique_pm = PatternMatcher([(UPat(Ops.CONST, src=(UPat(Ops.UNIQUE), UPat(Ops.DEVICE, name="d")), name="b"), lambda b,d: b.replace(src=(d,))),])
+_strip_unique_pm = PatternMatcher([
+  (UPat(Ops.CONST, src=(UPat(Ops.UNIQUE), UPat(Ops.DEVICE, name="d")), name="b"), lambda b,d: b.replace(src=(d,))),
+  (UPat(Ops.BUFFER, src=(UPat(Ops.UNIQUE), UPat(Ops.DEVICE, name="d")), name="b"), lambda b,d: b.replace(src=(UOp.unique(0), d))),
+])
 def _strip_unique(u: UOp) -> UOp: return graph_rewrite(u, _strip_unique_pm)
 
 def _t(*shape):
@@ -49,6 +52,14 @@ class TestTensorUOpBinop(unittest.TestCase):
   def test_mod_bool(self):       _check(self, _t(4).cast(dtypes.bool), lambda x: x % True)
   def test_fmod_bool(self):      _check(self, _t(4).cast(dtypes.bool), lambda x: x.fmod(True))
 
+class TestTensorUOpClone(unittest.TestCase):
+  def test_clone(self):
+    t = _t(3, 4).float()
+    self.assertIs(_strip_unique(t.clone().uop), _strip_unique(t.uop.clone()))
+  def test_clone_deviceless_const(self):
+    u = UOp.const(dtypes.float, 2.0)
+    self.assertIs(_strip_unique(Tensor(u).clone().uop), _strip_unique(u.clone()))
+
 class TestTensorUOpGetitem(unittest.TestCase):
   # ---- pure slice patterns ----
   def test_slice_full(self):           _check(self, _t(4), lambda x: x[slice(None)])
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index c5e68be489..fbdd120530 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -357,10 +357,9 @@ class Tensor(OpMixin):
     Creates a clone of this tensor allocating a separate buffer for the data.
     If `device` is specified, the clone is placed on that device.
     """
-    device = device or self.device
-    ret = self.empty_like(device=device)
+    ret = Tensor(self.uop.clone(device=device))
     if self.grad is not None: ret.grad = self.grad.clone(device=device)
-    return ret.assign(self.to(device))
+    return ret
 
   def to(self, device:str|tuple[str, ...]|None) -> Tensor:
     """
diff --git a/tinygrad/uop/ops.py b/tinygrad/uop/ops.py
index cfdc83aac2..e280554e88 100644
--- a/tinygrad/uop/ops.py
+++ b/tinygrad/uop/ops.py
@@ -703,6 +703,11 @@ class UOp(OpMixin, metaclass=UOpMetaClass):
     device = canonicalize_device(self.device if device is None else device)
     axis = self.axis if isinstance(device, tuple) else None
     return UOp.empty(self.shard_shape if axis is not None else self.shape, self.dtype if dtype is None else dtype, device, axis)
+  def clone(self, device=None) -> UOp:
+    device = device or self.device
+    ret = self.empty_like(device=device)
+    src = self if self.device is None or self.device == device else self.copy_to_device(device)
+    return ret.after(ret.store(src))
   @recursive_property
   def device(self) -> str|tuple[str, ...]|None:
     if self.op is Ops.DEVICE: return self.arg