diff --git a/test/external/external_test_opt.py b/test/external/external_test_opt.py
index d37922a6a8..88dbe2c1a5 100644
--- a/test/external/external_test_opt.py
+++ b/test/external/external_test_opt.py
@@ -63,7 +63,7 @@ class TestInferenceMinKernels(unittest.TestCase):
     for p in get_parameters(model): p.assign(np.zeros(p.shape, dtype=p.dtype.np))
     img = Tensor.randn(1, 3, 224, 224)
     # TODO: this seems very high
-    with CLCache(116):
+    with CLCache(115):
       model.forward(img).realize()
 
   def test_resnet(self):
diff --git a/tinygrad/lazy.py b/tinygrad/lazy.py
index e00f0969d7..b2ca2e5790 100644
--- a/tinygrad/lazy.py
+++ b/tinygrad/lazy.py
@@ -1,5 +1,4 @@
 from __future__ import annotations
-import math
 import operator
 from typing import Callable, Optional, Tuple, Union, List, Dict, Any, cast
 import sys, importlib, inspect, functools, pathlib
@@ -209,23 +208,11 @@ class LazyBuffer:
         return root.reshape(ret.st.shape)
     return ret
 
-  def _reduce_op(self:LazyBuffer, op:ReduceOps, new_shape:Tuple[int, ...]) -> LazyBuffer:
+  def reduce_op(self:LazyBuffer, op:ReduceOps, new_shape:Tuple[int, ...]) -> LazyBuffer:
     if self.shape == tuple(new_shape): return self
     srcs = _push_movement_ops((self,)) if SHUFFLE_MOVEMENT_OPS else (self,)
     return create_lazybuffer(self.device, ShapeTracker(new_shape), ReduceOps, LazyOp(op, srcs, new_shape), self.dtype)
 
-  def reduce_op(self:LazyBuffer, op:ReduceOps, new_shape:Tuple[int, ...]) -> LazyBuffer:
-    if prod(self.shape) // prod(new_shape) > 8192:
-      reduced_dimensions = [(i, math.gcd(256, old), stride) for i, (old, new, stride) in enumerate(zip(self.shape, new_shape, self.st.real_strides())) if old != new]
-      dimension_to_split, divisor, _ = max(reduced_dimensions, key=lambda v: v[1]//(v[2] or math.inf) ) # heuristic -> choose largest divisor to split on, penalize large strides
-
-      intermediate_input_shape = self.shape[:dimension_to_split] + (self.shape[dimension_to_split]//divisor, divisor) + self.shape[dimension_to_split+1:]
-      intermediate_output_shape = self.shape[:dimension_to_split] + (self.shape[dimension_to_split]//divisor, 1) + self.shape[dimension_to_split+1:]
-      final_input_shape = self.shape[:dimension_to_split] + (self.shape[dimension_to_split]//divisor,) + self.shape[dimension_to_split+1:]
-
-      return self.reshape(intermediate_input_shape)._reduce_op(op, intermediate_output_shape).reshape(final_input_shape)._reduce_op(op, new_shape)
-    return self._reduce_op(op, new_shape)
-
   def reshape(self:LazyBuffer, arg:Tuple[int, ...]) -> LazyBuffer:
     if self.shape == arg: return self
     if not self.realized and self.op.op == MovementOps.RESHAPE: