diff --git a/test/inductor/test_max_autotune.py b/test/inductor/test_max_autotune.py
index dcbac368157..0d6750add17 100644
--- a/test/inductor/test_max_autotune.py
+++ b/test/inductor/test_max_autotune.py
@@ -28,9 +28,8 @@ from torch._inductor.autotune_process import (
     TuningProcessPool,
 )
 from torch._inductor.graph import GraphLowering
-from torch._inductor.ir import Buffer, ChoiceCaller, FixedLayout, InputBuffer
+from torch._inductor.ir import Buffer, ChoiceCaller, FixedLayout
 from torch._inductor.kernel.mm_plus_mm import aten_mm_plus_mm
-from torch._inductor.kernel_inputs import MMKernelInputs
 from torch._inductor.select_algorithm import (
     add_feedback_saver,
     AlgorithmSelectorCache,
@@ -76,7 +75,7 @@ from torch.testing._internal.inductor_utils import (
 )
 
 
-torch.backends.cuda.matmul.allow_tf32 = True
+torch.set_float32_matmul_precision("high")
 if HAS_CUDA_AND_TRITON:
     torch.cuda.memory._set_allocator_settings("expandable_segments:False")
 
@@ -2077,39 +2076,6 @@ class TestMaxAutotuneRemoteCache(TestCase):
             global_stats.report()
             self.assertEqual(global_stats.autotune_remote, Stats(2, 3, 2))
 
-    def test_get_mm_configs_float32_precision_ieee(self):
-        """Test that configs returned from choices.get_mm_configs use float32_precision == ieee."""
-        from torch._inductor.choices import InductorChoices
-        from torch._inductor.graph import GraphLowering
-        from torch._inductor.ir import FlexibleLayout
-        from torch.fx.experimental.proxy_tensor import make_fx
-
-        # Create a simple graph to get proper context
-        gm = make_fx(lambda: torch.zeros(2, 3))()
-        graph = GraphLowering(gm)
-
-        with V.set_graph_handler(graph):
-            device = torch.device(f"{GPU_TYPE}:0")
-            mat1 = InputBuffer(
-                name="mat1",
-                layout=FixedLayout(device, torch.float32, [64, 128], [128, 1]),
-            )
-            mat2 = InputBuffer(
-                name="mat2",
-                layout=FixedLayout(device, torch.float32, [128, 64], [64, 1]),
-            )
-            kernel_inputs = MMKernelInputs([mat1, mat2])
-            output_layout = FlexibleLayout(device, torch.float32, [64, 64])
-
-            choices = InductorChoices()
-            configs = list(
-                choices.get_mm_configs(kernel_inputs, output_layout, "mm", "mm")
-            )
-
-            for cfg in configs:
-                self.assertIn("ALLOW_TF32", cfg)
-                self.assertEqual(cfg["ALLOW_TF32"], True)
-
 
 class _TestTritonTemplateCaller(TritonTemplateCaller):
     def __init__(self, bmreq: _TestBenchmarkRequest):