diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index 571d111f42..35b42bc261 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -2946,13 +2946,15 @@ class Tensor(OpMixin):
 
   # ***** broadcasted elementwise ops *****
 
-  def _broadcasted(self, y:Tensor|ConstType|UOp, reverse:bool=False, backward_cast:bool=True) -> tuple[Tensor, Tensor]:
+  def ufix(self, x) -> Tensor:
+    # TODO: x:ConstType|UOp does not work because mixin only accepts Self | ConstType
+    assert isinstance(x, (*get_args(ConstType), UOp)), f"{type(x)=}, {x=}"
+    dtype = self.dtype if dtypes.is_float(self.dtype) or (dtypes.is_int(self.dtype) and isinstance(x, (int, InvalidType))) else None
+    return Tensor(x, self.device, dtype, requires_grad=False)
+
+  def _broadcasted(self, y:Tensor|ConstType|UOp, reverse:bool=False) -> tuple[Tensor, Tensor]:
     x: Tensor = self
-    if not isinstance(y, Tensor):
-      # make y a Tensor
-      assert isinstance(y, (*get_args(ConstType), UOp)), f"{type(y)=}, {y=}"
-      y_dtype = x.dtype if dtypes.is_float(x.dtype) or (dtypes.is_int(x.dtype) and isinstance(y, (int, InvalidType))) else None
-      y = Tensor(y, x.device, y_dtype, requires_grad=False)
+    if not isinstance(y, Tensor): y = x.ufix(y)
 
     if x.dtype != y.dtype:
       output_dtype = least_upper_dtype(x.dtype, y.dtype)
@@ -2965,8 +2967,8 @@ class Tensor(OpMixin):
 
     # broadcast
     # NOTE: the backward cast is no-op in forward and uses sum_acc_dtype in the backward sum
-    return x.cast(sum_acc_dtype(x.dtype) if backward_cast else x.dtype)._broadcast_to(out_shape).cast(x.dtype), \
-           y.cast(sum_acc_dtype(y.dtype) if backward_cast else y.dtype)._broadcast_to(out_shape).cast(y.dtype)
+    return x.cast(sum_acc_dtype(x.dtype))._broadcast_to(out_shape).cast(x.dtype), \
+           y.cast(sum_acc_dtype(y.dtype))._broadcast_to(out_shape).cast(y.dtype)
 
   def sub(self, x:Tensor|ConstType, reverse=False) -> Tensor:
     """