diff --git a/tests/test_core.py b/tests/test_core.py
index 3cccdb7..ac14578 100644
--- a/tests/test_core.py
+++ b/tests/test_core.py
@@ -50,7 +50,7 @@ def construct(m: int, k: int, n: int) -> \
 def construct_contiguous_grouped(num_groups: int, expected_m_per_group: int, k: int, n: int) -> \
         Tuple[int, Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor], torch.Tensor, torch.Tensor, torch.Tensor]:
     alignment = get_m_alignment_for_contiguous_layout()
-    group_ms = [int(expected_m_per_group * random.uniform(0.7, 1.3))]
+    group_ms = [int(expected_m_per_group * random.uniform(0.7, 1.3)) for _ in range(num_groups)]
     m = sum([ceil_div(x, alignment) * alignment for x in group_ms])
 
     x = torch.randn((m, k), device='cuda', dtype=torch.bfloat16)
@@ -180,7 +180,7 @@ def test_gemm() -> None:
                 deep_gemm.gemm_fp8_fp8_bf16_nt(x_fp8, y_fp8, out)
 
             t = bench_kineto(test_func, 'fp8_gemm', suppress_kineto_output=True)
-            print(f' > Performance (m={m:5}, n={n:5}, k={k:5}): {t * 1e6:4.0f} us | '
+            print(f' > Perf (m={m:5}, n={n:5}, k={k:5}): {t * 1e6:4.0f} us | '
                   f'throughput: {2 * m * n * k / t / 1e12:4.0f} TFLOPS, '
                   f'{(m * k + k * n + m * n * 2) / 1e9 / t:4.0f} GB/s')
     print()
@@ -189,7 +189,9 @@ def test_gemm() -> None:
 def test_m_grouped_gemm_contiguous() -> None:
     print('Testing grouped contiguous GEMM:')
 
-    for num_groups, expected_m_per_group, k, n in ((4, 8192, 7168, 4096), (4, 8192, 2048, 7168), (8, 4096, 7168, 4096), (8, 4096, 2048, 7168)):
+    for num_groups, expected_m_per_group, k, n in ((4, 8192, 7168, 4096), (4, 8192, 2048, 7168),
+                                                   (8, 4096, 7168, 4096), (8, 4096, 2048, 7168),
+                                                   (32, 256, 7168, 4096), (32, 256, 2048, 7168)):
         # NOTES: we should mask the unfilled part before calculating difference
         m, x_fp8, y_fp8, m_indices, out, ref_out = construct_contiguous_grouped(num_groups, expected_m_per_group, k, n)
         deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_contiguous(x_fp8, y_fp8, out, m_indices)
@@ -203,7 +205,7 @@ def test_m_grouped_gemm_contiguous() -> None:
 
         t = bench_kineto(test_func, 'fp8_gemm', suppress_kineto_output=True)
         valid_m = (m_indices != -1).sum().item()
-        print(f' > Performance ({num_groups=}, expected_m_per_group={expected_m_per_group:4}, n={n:4}, k={k:4}): {t * 1e6:4.0f} us | '
+        print(f' > Perf ({num_groups=:2}, {expected_m_per_group=:4}, n={n:4}, k={k:4}): {t * 1e6:4.0f} us | '
               f'throughput: {2 * valid_m * n * k / t / 1e12:4.0f} TFLOPS, '
               f'{(valid_m * k + num_groups * k * n + valid_m * n * 2) / 1e9 / t:4.0f} GB/s')
     print()
@@ -231,7 +233,7 @@ def test_m_grouped_gemm_masked() -> None:
             # noinspection PyUnboundLocalVariable
             valid_m = masked_m.sum().item()
             t = bench_kineto(test_func, 'fp8_gemm', suppress_kineto_output=True)
-            print(f' > Performance ({num_groups=}, expected_m_per_group={expected_m_per_group:4}, n={n:4}, k={k:4}): {t * 1e6:4.0f} us | '
+            print(f' > Perf ({num_groups=}, expected_m_per_group={expected_m_per_group:4}, n={n:4}, k={k:4}): {t * 1e6:4.0f} us | '
                   f'throughput: {2 * valid_m * n * k / t / 1e12:4.0f} TFLOPS, '
                   f'{(valid_m * k + num_groups * k * n + valid_m * n * 2) / 1e9 / t:4.0f} GB/s')
     print()