diff --git a/axlearn/common/host_array_test.py b/axlearn/common/host_array_test.py
index 378e734d9..73b27ddc9 100644
--- a/axlearn/common/host_array_test.py
+++ b/axlearn/common/host_array_test.py
@@ -22,31 +22,31 @@
     host_to_global_device_array,
 )
 
+
 def is_supported(
     platform: str,
     mesh_shape: tuple[int, int],
     global_batch_size: int,
     data_partition: DataPartitionType,
 ):
-    return (
-        is_supported_platform(platform)
-        and is_supported_mesh_shape(mesh_shape)
-        and (
-            data_partition == DataPartitionType.REPLICATED
-            or global_batch_size % jax.device_count() == 0
-        )
-    )
+    if not is_supported_platform(platform):
+        return False, f'Platform "{platform}" not supported with devices {jax.devices()}.'
+    if not is_supported_mesh_shape(mesh_shape):
+        return False, f'Mesh shape "{mesh_shape}" not supported with device_count "{jax.device_count()}".'
+    if data_partition != DataPartitionType.REPLICATED:
+        return False, f'Data partition is "{data_partition}", expected "DataPartitionType.REPLICATED".'
+    if global_batch_size % jax.device_count() != 0:
+        return False, 'Global batch has to be divisible with number of devices. Global batch is "{global_batch_size}", number of devices is "{jax.device_count()}".'
+    return True , ""
+
 
 class HostArrayTest(TestCase):
     @parameterized.parameters(
-        filter(
-            lambda params: is_supported(*params),
-            itertools.product(
-                ("cpu", "tpu"),  # platform,
-                ((1, 1), (4, 1), (2, 2), (8, 1), (4, 2), (16, 4)),  # mesh_shape
-                (1, 16),  # global_batch_size
-                (DataPartitionType.FULL, DataPartitionType.REPLICATED, DataPartitionType,BATCH),  # data_partition
-            ),
+        itertools.product(
+            ("cpu", "tpu"),  # platform,
+            ((1, 1), (4, 1), (2, 2), (8, 1), (4, 2)),  # mesh_shape
+            (1, 16),  # global_batch_size
+            (DataPartitionType.FULL, DataPartitionType.REPLICATED, DataPartitionType.BATCH),  # data_partition
         )
     )
     def test_global_host_array_conversion(
@@ -63,6 +63,9 @@ def test_global_host_array_conversion(
             global_batch_size,
             data_partition,
         )
+        supported, reason = is_supported(platform, mesh_shape, global_batch_size, data_partition)
+        if not supported:
+            pytest.skip(reason)
         devices = mesh_utils.create_device_mesh(mesh_shape)
         mesh = jax.sharding.Mesh(devices, ("data", "model"))
         logging.info("Global mesh: %s", mesh)