diff --git a/python/tests/test_gemm.py b/python/tests/test_gemm.py
index b2ce97cb2..5e6cd421c 100644
--- a/python/tests/test_gemm.py
+++ b/python/tests/test_gemm.py
@@ -43,8 +43,7 @@ def matmul_no_scf_kernel(
     for trans_b in [False, True]
 ])
 def test_gemm_no_scf(SHAPE, NUM_WARPS, TRANS_A, TRANS_B):
-    if not valid_on_Volta(NUM_WARPS, TRANS_A, TRANS_B):
-        pytest.skip("Not valid on Volta")
+    guard_for_volta(NUM_WARPS, TRANS_A, TRANS_B)
 
     SIZE_M, SIZE_N, SIZE_K = SHAPE
     if (TRANS_A):
@@ -84,8 +83,7 @@ def test_gemm_no_scf(SHAPE, NUM_WARPS, TRANS_A, TRANS_B):
     for trans_b in [False, True]
 ])
 def test_gemm_no_scf_int8(SHAPE, NUM_WARPS, TRANS_A, TRANS_B):
-    if not valid_on_Volta(NUM_WARPS, TRANS_A, TRANS_B, is_int8=True):
-        pytest.skip("Not valid on Volta")
+    guard_for_volta(NUM_WARPS, TRANS_A, TRANS_B, is_int8=True)
 
     SIZE_M, SIZE_N, SIZE_K = SHAPE
 
@@ -201,8 +199,7 @@ def get_proper_err(a, b, golden):
     [128, 64, 128, 4, 128, 64, 32, False, True],
 ])
 def test_gemm(SIZE_M, SIZE_N, SIZE_K, NUM_WARPS, BLOCK_SIZE_M, BLOCK_SIZE_N, BLOCK_SIZE_K, TRANS_A, TRANS_B):
-    if not valid_on_Volta(NUM_WARPS, TRANS_A, TRANS_B):
-        pytest.skip("Not valid on Volta")
+    guard_for_volta(NUM_WARPS, TRANS_A, TRANS_B)
 
     if (TRANS_A):
         a = torch.randn((SIZE_K, SIZE_M), device='cuda', dtype=torch.float16).T
@@ -279,8 +276,7 @@ def test_gemm_fp32(M, N, K, num_warps, block_M, block_N, block_K, allow_tf32):
         c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
         tl.store(c_ptrs, accumulator, c_mask)
 
-    if not valid_on_Volta(num_warps, trans_a=False, trans_b=False, is_tf32=allow_tf32):
-        pytest.skip("Not valid on Volta")
+    guard_for_volta(num_warps, trans_a=False, trans_b=False, is_tf32=allow_tf32)
 
     # Configure the pytorch counterpart
     torch.backends.cuda.matmul.allow_tf32 = allow_tf32
@@ -306,16 +302,17 @@ def test_gemm_fp32(M, N, K, num_warps, block_M, block_N, block_K, allow_tf32):
         torch.testing.assert_close(c, golden, rtol=max(1e-4, 1.5 * golden_rel_err), atol=max(1e-4, 1.5 * golden_abs_err))
 
 
-def valid_on_Volta(num_warps, trans_a, trans_b, is_int8=False, is_tf32=False):
+def guard_for_volta(num_warps, trans_a, trans_b, is_int8=False, is_tf32=False):
     '''
     Tell whether the test case is valid on Volta GPU.
     Some features are WIP, so the corresponding support are missing.
     '''
-    if is_int8 or is_tf32:
-        return False
-
     capability = torch.cuda.get_device_capability()
     is_on_Volta = capability[0] < 8
     # TODO[Superjomn]: Remove the constraints below when features are ready
+    is_feature_supported = not (is_int8 or is_tf32)
     is_feature_ready = not (trans_a or trans_b)
-    return is_on_Volta and is_feature_ready
+
+    if is_on_Volta:
+        if (not is_feature_supported) or (not is_feature_ready):
+            pytest.skip("Not valid on Volta")