diff --git a/test/null/test_tensor_uop_mixin.py b/test/null/test_tensor_uop_mixin.py
index 9acc456799..1ab37a9b6d 100644
--- a/test/null/test_tensor_uop_mixin.py
+++ b/test/null/test_tensor_uop_mixin.py
@@ -228,6 +228,10 @@ class TestTensorUOpRand(unittest.TestCase):
   def test_threefry_random_bits(self):
     key, c0, c1 = UOp.empty((2,), dtype=dtypes.uint32), UOp.arange(4, dtype=dtypes.uint32), UOp.arange(4, dtype=dtypes.uint32)
     self.assertIs(Tensor._threefry_random_bits(Tensor(key), Tensor(c0), Tensor(c1)).uop, UOp._threefry_random_bits(key, c0, c1))
+  def test_rand(self):
+    k, c = UOp.empty((2,), dtype=dtypes.uint32), UOp.zeros(2, dtype=dtypes.uint32)
+    self.assertIs(Tensor._rand(Tensor(k), Tensor(c), (2, 2), dtypes.float32).uop, UOp._rand(k, c, (2, 2), dtypes.float32))
+    self.assertIs(Tensor._rand(Tensor(k), Tensor(c), (0, 3), dtypes.float32).uop, UOp._rand(k, c, (0, 3), dtypes.float32))
 
 class TestTensorUOpGather(unittest.TestCase):
   def _check(self, t, dim, idx):
diff --git a/tinygrad/mixin/rand.py b/tinygrad/mixin/rand.py
index c846830bc0..52c6621a15 100644
--- a/tinygrad/mixin/rand.py
+++ b/tinygrad/mixin/rand.py
@@ -24,7 +24,7 @@ class RandMixin(OpMixin):
       counts0 = cls.arange(ceildiv(chunk_num, 2), dtype=dtypes.uint32)
       counts1 = counts0 + ceildiv(chunk_num, 2)
       bits.append(cls._threefry_random_bits(new_key, counts0, counts1)[:chunk_num])
-    return bits[0].cat(*bits[1:])
+    return bits[0].cat(*bits[1:]) if bits else counter[0:0]
 
   @staticmethod
   def _bits_to_rand(bits, shape:tuple[int, ...], dtype:DType):
@@ -33,3 +33,9 @@ class RandMixin(OpMixin):
     uint_bits = bits.bitcast(uint_dtype)
     float_one_bits = uint_bits.const_like(1).cast(dtype).bitcast(uint_dtype)
     return uint_bits.rshift(dtype.bitsize - nmant).bitwise_or(float_one_bits).bitcast(dtype)[:prod(shape)].sub(1).reshape(shape)
+
+  @classmethod
+  def _rand(cls, key:Self, counter:Self, shape:tuple[int, ...], dtype:DType, contiguous:bool=True) -> Self:
+    bits = cls.random_bits(key, counter, ceildiv(prod(shape) * dtype.itemsize, 4))
+    out = cls._bits_to_rand(bits, shape, dtype)
+    return out.contiguous() if contiguous else out
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index 043eb1292f..d7c66b4ae4 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -551,14 +551,8 @@ class Tensor(RandMixin):
     if not all_int(shape:=argfix(*shape)) or not all(s >= 0 for s in shape): raise ValueError(f"invalid input {shape=}")
     if device is not None and not isinstance(device, str): raise ValueError(f"rand only supports single device, got {device=}")
     device = cast(str, canonicalize_device(device))
-
-    # if shape has 0, return zero tensor
-    if (numel := prod(shape)) == 0: return Tensor.zeros(shape, device=device, dtype=dt)
-    num = ceildiv(numel * dt.itemsize, 4)
-    key, counter = Tensor._next_counter(device, num)
-    bits = Tensor.random_bits(key, counter, num)
-    out = Tensor._bits_to_rand(bits, shape, dt)
-    return out.contiguous() if contiguous else out
+    key, counter = Tensor._next_counter(device, ceildiv(prod(shape) * dt.itemsize, 4))
+    return Tensor._rand(key, counter, shape, dt, contiguous=contiguous)
 
   # ***** creation helper functions *****