diff --git a/tinygrad/gradient.py b/tinygrad/gradient.py
index 317510de1e..f4cb99f0eb 100644
--- a/tinygrad/gradient.py
+++ b/tinygrad/gradient.py
@@ -1,6 +1,6 @@
 from typing import cast
 import math, dataclasses
-from tinygrad.dtype import dtypes, sum_acc_dtype
+from tinygrad.dtype import dtypes
 from tinygrad.uop.ops import UOp, PatternMatcher, UPat, Ops, all_metadata
 from tinygrad.helpers import argsort
 
@@ -38,9 +38,7 @@ pm_gradient = PatternMatcher([
   (UPat(Ops.PAD, name="ret"), lambda ctx, ret: (ctx.shrink(tuple([(p[0], s+p[0]) for s,p in zip(ret.src[0].shape, ret.arg)])),)),
   (UPat(Ops.SHRINK, name="ret"), lambda ctx, ret: (ctx.pad(tuple([(p[0], s-p[1]) for s,p in zip(ret.src[0].shape, ret.arg)])),)),
   (UPat(Ops.FLIP, name="ret"), lambda ctx, ret: (ctx.flip(ret.arg),)),
-  # TODO: this cast can be removed by putting the casts around the EXPAND
-  (UPat(Ops.EXPAND, name="ret"), lambda ctx, ret:
-    (ctx.cast(sum_acc_dtype(ctx.dtype)).r(Ops.ADD, tuple(i for i,(si,so) in enumerate(zip(ret.src[0].shape, ret.arg)) if si!=so)).cast(ctx.dtype),)),
+  (UPat(Ops.EXPAND, name="ret"), lambda ctx, ret: (ctx.r(Ops.ADD, tuple(i for i,(si,so) in enumerate(zip(ret.src[0].shape, ret.arg)) if si!=so)),)),
   (UPat(Ops.MULTI, name="ret"), lambda ctx, ret: ctx.shard(ret.device, ret.axis).src),
   # there's no gradient for bitcast
   (UPat(Ops.BITCAST), lambda ctx: (None,)),
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index f32a87f606..f20f1ae527 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -3561,7 +3561,8 @@ class Tensor(MathTrait):
     # for each dimension, check either dim is 1, or it does not change
     if not all(resolve(s == ns) or resolve(s == 1) for s,ns in zip(shape, new_shape)):
       raise ValueError(f"cannot broadcast {self.shape} to {new_shape=}")
-    return self.reshape(shape)._apply_uop(UOp.expand, arg=new_shape)
+    # NOTE: this cast is no-op in forward and uses sum_acc_dtype in the backward sum
+    return self.reshape(shape).cast(sum_acc_dtype(self.dtype))._apply_uop(UOp.expand, arg=new_shape).cast(self.dtype)
 
   def _broadcasted(self, y:Tensor|ConstType|UOp, reverse:bool=False, match_dtype:bool=True) -> tuple[Tensor, Tensor]:
     x: Tensor = self