diff --git a/tests/python/quantization/test_quantization.py b/tests/python/quantization/test_quantization.py
index 08303c816af1..ce43ad92fda8 100644
--- a/tests/python/quantization/test_quantization.py
+++ b/tests/python/quantization/test_quantization.py
@@ -510,7 +510,6 @@ def test_optimal_threshold_adversarial_case():
 
 
 @with_seed()
-@unittest.skip("Flaky test: https://github.com/apache/incubator-mxnet/issues/11456")
 def test_get_optimal_thresholds():
     # Given an ndarray with elements following a uniform distribution, the optimal threshold
     # for quantizing the ndarray should be either abs(min(nd)) or abs(max(nd)).
@@ -519,11 +518,11 @@ def get_threshold(nd):
         max_nd = mx.nd.max(nd)
         return mx.nd.maximum(mx.nd.abs(min_nd), mx.nd.abs(max_nd)).asnumpy()
 
-    nd_dict = {'layer1': mx.nd.uniform(low=-10.532, high=11.3432, shape=(8, 3, 23, 23))}
+    nd_dict = {'layer1': mx.nd.uniform(low=-10.532, high=11.3432, shape=(8, 3, 23, 23), dtype=np.float64)}
     expected_threshold = get_threshold(nd_dict['layer1'])
     th_dict = mx.contrib.quant._get_optimal_thresholds(nd_dict)
     assert 'layer1' in th_dict
-    assert_almost_equal(np.array([th_dict['layer1'][1]]), expected_threshold, rtol=0.001, atol=0.001)
+    assert_almost_equal(np.array([th_dict['layer1'][1]]), expected_threshold, rtol=1e-2, atol=1e-4)
 
 
 if __name__ == "__main__":