diff --git a/tensorflow/compiler/tests/binary_ops_test.py b/tensorflow/compiler/tests/binary_ops_test.py
index c7be2c55de7..422695c374b 100644
--- a/tensorflow/compiler/tests/binary_ops_test.py
+++ b/tensorflow/compiler/tests/binary_ops_test.py
@@ -19,7 +19,6 @@ from __future__ import division
 from __future__ import print_function
 
 import itertools
-import os
 
 import numpy as np
 
@@ -1609,8 +1608,4 @@ class BinaryOpsTest(xla_test.XLATestCase):
 
 
 if __name__ == "__main__":
-  # TODO(b/130689556): XLA CPU does not honor inf/nan which causes problems
-  os.environ[
-      "XLA_FLAGS"] = "--xla_cpu_enable_fast_math=false " + os.environ.get(
-          "XLA_FLAGS", "")
   googletest.main()
diff --git a/tensorflow/compiler/tests/unary_ops_test.py b/tensorflow/compiler/tests/unary_ops_test.py
index d0e928a5ce6..85bf89c4f9e 100644
--- a/tensorflow/compiler/tests/unary_ops_test.py
+++ b/tensorflow/compiler/tests/unary_ops_test.py
@@ -347,17 +347,15 @@ class UnaryOpsTest(xla_test.XLATestCase):
           expected=np.array(
               [1.55740772, -2.18503986, -0.14254654, 1.15782128], dtype=dtype))
 
-      # TODO(b/130689556): Turn this on for CPU when we start honoring NaNs.
-      if self.device != "XLA_CPU":
-        self._assertOpOutputMatchesExpected(
-            math_ops.tanh,
-            np.array([[1, 2, 3, 4], [np.inf, -np.inf, np.nan, 20],
-                      [19, -19, 22, -22]],
-                     dtype=dtype),
-            expected=np.array(
-                [[0.76159418, 0.96402758, 0.99505478, 0.99932933],
-                 [1.0, -1.0, np.nan, 1.0], [1.0, -1.0, 1.0, -1.0]],
-                dtype=dtype))
+      self._assertOpOutputMatchesExpected(
+          math_ops.tanh,
+          np.array([[1, 2, 3, 4], [np.inf, -np.inf, np.nan, 20],
+                    [19, -19, 22, -22]],
+                   dtype=dtype),
+          expected=np.array(
+              [[0.76159418, 0.96402758, 0.99505478, 0.99932933],
+               [1.0, -1.0, np.nan, 1.0], [1.0, -1.0, 1.0, -1.0]],
+              dtype=dtype))
 
       self._assertOpOutputMatchesExpected(
           nn_ops.log_softmax,
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 13c58c74583..2fb22a89706 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -5122,8 +5122,6 @@ cuda_py_test(
     srcs = ["ops/nn_test.py"],
     python_version = "PY3",
     tags = ["no_windows"],
-    # TODO(b/130689556): Numerical differences due to fast math on CPU.
-    xla_enable_strict_auto_jit = False,
     deps = [
         ":array_ops",
         ":client_testlib",
diff --git a/tensorflow/python/kernel_tests/numerics_test.py b/tensorflow/python/kernel_tests/numerics_test.py
index 475badb6efe..eadb8ceff07 100644
--- a/tensorflow/python/kernel_tests/numerics_test.py
+++ b/tensorflow/python/kernel_tests/numerics_test.py
@@ -18,8 +18,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import os
-
 import numpy as np
 
 from tensorflow.python.framework import constant_op
@@ -133,8 +131,4 @@ class NumericsTest(test.TestCase):
 
 
 if __name__ == "__main__":
-  # TODO(b/130689556): XLA CPU does not honor inf/nan which causes problems
-  os.environ[
-      "XLA_FLAGS"] = "--xla_cpu_enable_fast_math=false " + os.environ.get(
-          "XLA_FLAGS", "")
   test.main()
diff --git a/tensorflow/python/ops/nn_test.py b/tensorflow/python/ops/nn_test.py
index 0088c04f909..477e0528c0d 100644
--- a/tensorflow/python/ops/nn_test.py
+++ b/tensorflow/python/ops/nn_test.py
@@ -1207,6 +1207,7 @@ class DataFormatVectorPermuteTest(test_lib.TestCase):
       y_val = self.evaluate(y)
       self.assertAllEqual(y_val, [4, 9])
 
+  @test_util.disable_xla("unsupported data format")
   def testNHWCToWHCN(self):
     x_val = [7, 4, 9, 3]
     x = constant_op.constant(x_val)
@@ -1215,6 +1216,7 @@ class DataFormatVectorPermuteTest(test_lib.TestCase):
       y_val = self.evaluate(y)
       self.assertAllEqual(y_val, [9, 4, 3, 7])
 
+  @test_util.disable_xla("unsupported data format")
   def testNHWCToWHCN_Size2(self):
     x_val = [4, 9]
     x = constant_op.constant(x_val)