diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index e207656c4213..bcc5e6323cb4 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -469,23 +469,21 @@ def rand(*shape, device:Optional[str]=None, dtype:Optional[DTypeLike]=None, cont
     if device is not None and not isinstance(device, str): raise ValueError(f"rand only supports single device, got {device=}")
     _device = device = Device.canonicalize(device)
 
+    # if shape has 0, return zero tensor
+    if (num := ceildiv(((num_ := prod(shape)) * dtype.itemsize), 4)) == 0: return Tensor.zeros(shape, device=_device, dtype=dtype, **kwargs)
+
     # when using MOCKGPU and NV generate rand on CLANG
     if getenv("MOCKGPU") and device.startswith("NV"): device = "CLANG"
 
-    # generate per device seeds and rng counter if we haven't seen this device yet
     if device not in Tensor._device_seeds:
+      # generate per device seeds and rng counter if we haven't seen this device yet
       Tensor._device_seeds[device] = Tensor([((Tensor._seed & 0xffffffff) << 32) \
         | int.from_bytes(hashlib.sha256(len(Tensor._device_seeds).to_bytes(4, "big")).digest(), "big") & 0xffffffff],
                                             device=device, dtype=dtypes.uint64, requires_grad=False)
       Tensor._device_rng_counters[device] = Tensor([0], device=device, dtype=dtypes.uint32, requires_grad=False)
-      had_counter = False
-    else: had_counter = True
-
-    # if shape has 0, return zero tensor
-    if (num := ceildiv(((num_ := prod(shape)) * dtype.itemsize), 4)) == 0: return Tensor.zeros(shape, device=_device, dtype=dtype, **kwargs)
-
-    # increment rng counter for devices
-    if had_counter: Tensor._device_rng_counters[device].assign(Tensor._device_rng_counters[device] + num).contiguous()
+    else:
+      # have seen this device, increment its rng counter
+      Tensor._device_rng_counters[device].assign(Tensor._device_rng_counters[device] + num).contiguous()
 
     # threefry random bits
     counts0 = (Tensor.arange(ceildiv(num, 2), device=device, dtype=dtypes.uint32, requires_grad=False)+Tensor._device_rng_counters[device])