diff --git a/tests/test_zero/test_low_level/test_zero1_2.py b/tests/test_zero/test_low_level/test_zero1_2.py
index 8df35bdaa..cda51c4ef 100644
--- a/tests/test_zero/test_low_level/test_zero1_2.py
+++ b/tests/test_zero/test_low_level/test_zero1_2.py
@@ -51,7 +51,8 @@ def split_ddp_grad(grad, world_size):
     return splited_grad
 
 
-def exam_zero_1_2():
+@parameterize("fp8_communication", [True, False])
+def exam_zero_1_2(fp8_communication: bool):
     """
     In this test, we want to test whether zero stage 1 and 2
     deliver the same numerical results despite different communication
@@ -73,10 +74,18 @@ def exam_zero_1_2():
     zero1_optimizer = torch.optim.Adam(zero1_model.parameters(), lr=1)
     zero2_optimizer = torch.optim.Adam(zero2_model.parameters(), lr=1)
     zero1_optimizer = LowLevelZeroOptimizer(
-        zero1_optimizer, overlap_communication=True, initial_scale=128, verbose=True
+        zero1_optimizer,
+        overlap_communication=True,
+        initial_scale=128,
+        verbose=True,
+        fp8_communication=fp8_communication,
     )
     zero2_optimizer = LowLevelZeroOptimizer(
-        zero2_optimizer, overlap_communication=True, partition_grad=True, initial_scale=128
+        zero2_optimizer,
+        overlap_communication=True,
+        partition_grad=True,
+        initial_scale=128,
+        fp8_communication=fp8_communication,
     )
     # create data
     seed_all(2001 + local_rank)
@@ -97,7 +106,10 @@ def exam_zero_1_2():
         if g1 is None or g2 is None:
             assert g1 is None and g2 is None
             continue
-        assert torch.allclose(g1, g2)
+        if fp8_communication:
+            loose_close(g1, g2, dtype=torch.float16)
+        else:
+            assert torch.allclose(g1, g2)
 
     # step
     zero1_optimizer.step()
@@ -105,7 +117,8 @@ def exam_zero_1_2():
 
     # check updated param
     for z1p, z2p in zip(zero1_model.parameters(), zero2_model.parameters()):
-        assert torch.allclose(z1p, z2p)
+        if not fp8_communication:
+            assert torch.allclose(z1p, z2p)
 
 
 @parameterize("dtype", [torch.float16, torch.bfloat16])