diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index df5acde6e0..5177a9f8ed 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -43,13 +43,6 @@ def _loadop(op, shape:Tuple[sint,...], dtype:DType, device:Union[str, Tuple[str,
   if isinstance(device, str): return LazyBuffer.loadop(op, shape, dtype, device, arg, src)
   return MultiLazyBuffer([LazyBuffer.loadop(op, shape, dtype, d, arg, src) for d in device], None)
 
-def _fromcpu(x: np.ndarray) -> LazyBuffer:
-  ret = LazyBuffer.loadop(LoadOps.EMPTY, x.shape, dtypes.from_np(x.dtype), "NPY")
-  # fake realize
-  ret.buffer.allocate(x)
-  del ret.srcs
-  return ret
-
 def _frompy(x:Union[List, Tuple, bytes], dtype:DType) -> LazyBuffer:
   if isinstance(x, bytes): ret, data = LazyBuffer.loadop(LoadOps.EMPTY, (len(x),), dtype, "PYTHON"), x
   else:
@@ -123,10 +116,17 @@ class Tensor:
       if dtype is None: dtype = dtypes.from_py(data)
       if dtype == dtypes.bfloat16: data = Tensor(_frompy(data, dtypes.float32), device=device).cast(dtypes.bfloat16).lazydata
       else: data = _frompy(data, dtype)
+    elif data is None: data = _loadop(LoadOps.EMPTY, (0,), dtype or dtypes.default_float, device)
     elif isinstance(data, np.ndarray):
       if data.shape == (): data = _loadop(LoadOps.CONST, tuple(), dtype or dtypes.from_np(data.dtype), device, data.item())
-      else: data = _fromcpu(data.astype(dtype.np) if dtype is not None and dtype.np is not None else data)
-    elif data is None: data = _loadop(LoadOps.EMPTY, (0,), dtype or dtypes.default_float, device)
+      else:
+        def _fromnp(x: np.ndarray) -> LazyBuffer:
+          ret = LazyBuffer.loadop(LoadOps.EMPTY, x.shape, dtypes.from_np(x.dtype), "NPY")
+          # fake realize
+          ret.buffer.allocate(x)
+          del ret.srcs
+          return ret
+        data = _fromnp(data.astype(dtype.np) if dtype is not None and dtype.np is not None else data)
 
     # by this point, it has to be a LazyBuffer
     if not isinstance(data, (LazyBuffer, MultiLazyBuffer)):