diff --git a/test/distributed/tensor/debug/test_debug_mode.py b/test/distributed/tensor/debug/test_debug_mode.py
index eb20889f2a04f..e3030bd5982fd 100644
--- a/test/distributed/tensor/debug/test_debug_mode.py
+++ b/test/distributed/tensor/debug/test_debug_mode.py
@@ -52,10 +52,14 @@
 @requires_accelerator
 
 def _get_accelerator_memory():
-    try:
-        return torch.accelerator.get_memory_info(0)[1]
-    except (NotImplementedError):
-        return 0  # Return 0, as that would help skip the test is not skipped
+    if not torch.accelerator.is_available():
+        return -1
+    device = torch.accelerator.current_accelerator()
+    if not hasattr(torch, device.type) or not hasattr(
+        getattr(torch, device.type), "mem_get_info"
+    ):
+        return -1
+    return torch.accelerator.get_memory_info(0)[1]
 
 class TestDTensorDebugMode(TestCase):
     def tearDown(self):
@@ -917,12 +921,13 @@ def test_check_hash_mismatches(self):
             [call["call"] for call in mismatches], ["aten::sin", "aten::sum"]
         )
 
-    @unittest.skipIf(
-        not torch.accelerator.is_available()
-        or _get_accelerator_memory() < 2**26,
-        "Being conservative, test peak memory is 25MB?",
-    )
     def test_tensor_hash_redistribute(self):
+
+        mem = _get_accelerator_memory()
+        if mem ==-1:
+            self.skipTest("No accelerator available or memory query not supported")
+        if mem < 2**26:
+            self.skipTest("Requires accelerator with at least 64MB memory")
         # test that hashing collectives gives correct results
         mesh = DeviceMesh(self.device_type, list(range(self.world_size)))