diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index dd4a935e0b..517f54ab15 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -605,7 +605,7 @@ jobs:
         deps: testing_minimal
     - name: Test CPU=1 RANGEIFY=1
       # TODO: add more passing tests here
-      run: CPU=1 RANGEIFY=1 python3 -m pytest -n auto test/test_tiny.py test/test_rangeify.py test/test_ops.py -k "not test_gather_failure" --durations 20
+      run: CPU=1 RANGEIFY=1 python3 -m pytest -n auto test/test_tiny.py test/test_rangeify.py test/test_ops.py --durations 20
 
   testdevectorize:
     name: Linux (devectorize)
diff --git a/test/test_ops.py b/test/test_ops.py
index 64d812b2cb..2d59a5a978 100644
--- a/test/test_ops.py
+++ b/test/test_ops.py
@@ -2804,11 +2804,7 @@ class TestOps(unittest.TestCase):
     helper_test_op(None, lambda x: x.gather(dim=0, index=torch.tensor([2, 1, 0, 1, 2], requires_grad=False)),
                          lambda x: x.gather(dim=0, index=Tensor([2, 1, 0, 1, 2])),
                          vals=[[1., 2., 3.]])
-
-  @unittest.expectedFailure
-  @unittest.skipIf(torch._C._get_privateuse1_backend_name() == "tiny", 'results in a success instead of a failure')
-  def test_gather_failure(self):
-    # gather with inf values do not work, other values results in nan
+    # gather with inf values
     helper_test_op(None, lambda x: x.gather(dim=0, index=torch.tensor([2, 1, 0, 1, 2], requires_grad=False)),
                          lambda x: x.gather(dim=0, index=Tensor([2, 1, 0, 1, 2])),
                          vals=[[-float("inf"), 2., 3.]])
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index 04a5726ea4..c5e968e7e4 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -1303,7 +1303,7 @@ class Tensor(MathTrait):
     assert all(s >= i for d,(s,i) in enumerate(zip(self.shape, index.shape)) if d != dim), "requires self.shape[d] >= index.shape[d] for all d != dim"
     index = index.to(self.device)
     x = self.shrink(tuple((0, i) if d != dim else None for d,i in enumerate(index.shape))).unsqueeze(-1).transpose(-1, dim)
-    return (x * index.unsqueeze(-1)._one_hot_along_dim(self.shape[dim])).sum(-1, dtype=self.dtype)
+    return (index.unsqueeze(-1)._one_hot_along_dim(self.shape[dim]).where(x, 0)).sum(-1, dtype=self.dtype)
 
   def cat(self:Tensor, *args:Tensor, dim:int=0) -> Tensor:
     """