diff --git a/test/test_tensor.py b/test/test_tensor.py
index a00a770a52..fa48c52092 100644
--- a/test/test_tensor.py
+++ b/test/test_tensor.py
@@ -300,6 +300,13 @@ class TestTinygrad(unittest.TestCase):
         data = _generate_data(depth)
         np.testing.assert_allclose(Tensor(data).numpy(), np.array(data))
 
+  def test_tensor_bytes(self):
+    data = b"abc123"
+    t = Tensor(data)
+    assert t.dtype == dtypes.uint8
+    assert t.shape == (6,)
+    np.testing.assert_equal(t.numpy(), list(data))
+
   def test_tensor_copy(self):
     x = copy.deepcopy(Tensor.ones((3,3,3)))
     np.testing.assert_allclose(x.numpy(), np.ones((3,3,3)))
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index 621bb24e9a..df5acde6e0 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -50,11 +50,14 @@ def _fromcpu(x: np.ndarray) -> LazyBuffer:
   del ret.srcs
   return ret
 
-def _frompy(x:Union[List, Tuple], dtype:DType) -> LazyBuffer:
-  ret = LazyBuffer.loadop(LoadOps.EMPTY, get_shape(x), dtype, "PYTHON")
+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:
+    ret = LazyBuffer.loadop(LoadOps.EMPTY, get_shape(x), dtype, "PYTHON")
+    assert dtype.fmt is not None, f"{dtype=} has None fmt"
+    data = struct.pack(f"@{ret.size}{dtype.fmt}", *fully_flatten(x))
   # fake realize
-  assert dtype.fmt is not None, f"{dtype=} has None fmt"
-  ret.buffer.allocate(memoryview(struct.pack(f"@{ret.size}{dtype.fmt}", *fully_flatten(x))))
+  ret.buffer.allocate(memoryview(data))
   del ret.srcs
   return ret
 
@@ -115,7 +118,7 @@ class Tensor:
     if isinstance(data, LazyBuffer): assert dtype is None or dtype == data.dtype, "dtype doesn't match, and casting isn't supported"
     elif isinstance(data, get_args(ConstType)): data = _loadop(LoadOps.CONST, tuple(), dtype or dtypes.from_py(data), device, data)
     elif isinstance(data, Variable): data = _loadop(LoadOps.CONST, tuple(), dtype or dtypes.from_py(data.unbind()[1]), device, data)
-    elif isinstance(data, bytes): data = _fromcpu(np.frombuffer(data, np.uint8))
+    elif isinstance(data, bytes): data = _frompy(data, dtypes.uint8)
     elif isinstance(data, (list, tuple)):
       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