diff --git a/test/test_linearizer.py b/test/test_linearizer.py
index 6f1031a99d..09d46983e5 100644
--- a/test/test_linearizer.py
+++ b/test/test_linearizer.py
@@ -72,10 +72,16 @@ class TestLinearizer(unittest.TestCase):
     num_ops = len([uop for uop in k.uops if uop.uop in [UOps.LOAD, UOps.ALU]])
     assert num_ops <= 0, "more load or alu uops than needed"
 
-  def test_tensor_cores(self):
-    if Device.DEFAULT not in tensor_cores:
-      self.skipTest("No tensor cores for device")
+  def test_sum_acc_dtype(self):
+    for tensor_dtype, acc_dtype in ((dtypes.bool, dtypes.int), (dtypes.int16, dtypes.int), (dtypes.float16, dtypes.float), (dtypes.bfloat16, dtypes.float)):
+      a = Tensor([1, 2, 3], dtype=tensor_dtype).sum()
+      k = Linearizer(a.lazydata.schedule()[-1].ast)
+      k.linearize()
+      local = [uop for uop in k.uops if uop.uop == UOps.DEFINE_ACC]
+      assert local[0].dtype == acc_dtype
 
+  @unittest.skipUnless(Device.DEFAULT in tensor_cores, "No tensor cores for device")
+  def test_tensor_cores(self):
     for tc in tensor_cores[Device.DEFAULT]:
       if tc.arch is not None and tc.arch != os.uname().machine: continue
       a, b = Tensor.rand(tc.dims[0], tc.dims[2], dtype=tc.dtype_in), Tensor.rand(tc.dims[2], tc.dims[1], dtype=tc.dtype_in)
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index 29ce411c9f..1c716394c0 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -515,9 +515,12 @@ class Tensor:
     return ret if keepdim else ret.reshape(shape=shape)
 
   def sum(self, axis=None, keepdim=False):
-    output_dtype = least_upper_dtype(self.dtype, dtypes.uint) if dtypes.is_unsigned(self.dtype) else \
-                   least_upper_dtype(self.dtype, dtypes.int) if (dtypes.is_int(self.dtype) or self.dtype==dtypes.bool) else self.dtype
-    return self.cast(output_dtype)._reduce(mlops.Sum, axis, keepdim)
+    acc_dtype = least_upper_dtype(self.dtype, dtypes.uint) if dtypes.is_unsigned(self.dtype) else \
+                least_upper_dtype(self.dtype, dtypes.int) if (dtypes.is_int(self.dtype) or self.dtype==dtypes.bool) else \
+                least_upper_dtype(self.dtype, dtypes.float)
+    # cast back to float16 or bfloat16 to match torch / jax behavior, but we use float for acc
+    output_dtype = self.dtype if self.dtype in (dtypes.float16, dtypes.bfloat16) else acc_dtype
+    return self.cast(acc_dtype)._reduce(mlops.Sum, axis, keepdim).cast(output_dtype)
 
   def max(self, axis=None, keepdim=False): return self._reduce(mlops.Max, axis, keepdim)
   def min(self, axis=None, keepdim=False): return -((-self).max(axis=axis, keepdim=keepdim))