diff --git a/deep_gemm/jit_kernels/gemm.py b/deep_gemm/jit_kernels/gemm.py
index 1ba413a..082c8ee 100644
--- a/deep_gemm/jit_kernels/gemm.py
+++ b/deep_gemm/jit_kernels/gemm.py
@@ -101,7 +101,14 @@ def get_best_configs(m: int, n: int, k: int, num_groups: int, num_sms: int,
     if m >= 1024 and is_tma_multicast_legal(n, best_block_n, 2, num_sms) and num_groups == 1:
         best_num_tma_multicast = 2
 
-    return best_block_m, best_block_n, best_num_stages, best_num_tma_multicast, best_smem_size
+    # Recompute the minimal number of SMs required
+    # NOTES: less L2 cache usage and less GPU frequency drop
+    num_waves = get_num_waves(best_block_m, best_block_n)
+    num_min_sms = ceil_div(ceil_div(m, best_block_m) * ceil_div(n, best_block_n) * num_groups, num_waves)
+    num_min_sms = ceil_div(max(num_min_sms, num_sms - 8), best_num_tma_multicast) * best_num_tma_multicast
+    assert num_min_sms <= num_sms and is_tma_multicast_legal(n, best_block_n, best_num_tma_multicast, num_min_sms)
+
+    return num_min_sms, best_block_m, best_block_n, best_num_stages, best_num_tma_multicast, best_smem_size
 
 
 def gemm_fp8_fp8_bf16_nt(lhs: Tuple[torch.Tensor, torch.Tensor],
@@ -151,7 +158,7 @@ def gemm_fp8_fp8_bf16_nt(lhs: Tuple[torch.Tensor, torch.Tensor],
     # Auto-tuning with compilation
     global includes, template
     num_sms = get_num_sms()
-    block_m, block_n, num_stages, num_tma_multicast, smem_size = get_best_configs(m, n, k, 1, num_sms)
+    num_sms, block_m, block_n, num_stages, num_tma_multicast, smem_size = get_best_configs(m, n, k, 1, num_sms)
     args = (lhs, lhs_scales, rhs, rhs_scales, out, m, torch.cuda.current_stream(), num_sms, smem_size)
     runtime = jit_tuner.compile_and_tune(
         name='gemm_fp8_fp8_bf16_nt',
diff --git a/deep_gemm/jit_kernels/m_grouped_gemm.py b/deep_gemm/jit_kernels/m_grouped_gemm.py
index 97fb636..140e165 100644
--- a/deep_gemm/jit_kernels/m_grouped_gemm.py
+++ b/deep_gemm/jit_kernels/m_grouped_gemm.py
@@ -84,7 +84,7 @@ def m_grouped_gemm_fp8_fp8_bf16_nt_contiguous(lhs: Tuple[torch.Tensor, torch.Ten
     # Auto-tuning with compilation
     global includes, template
     num_sms = get_num_sms()
-    block_m, block_n, num_stages, num_tma_multicast, smem_size = get_best_configs(m, n, k, 1, num_sms,
+    num_sms, block_m, block_n, num_stages, num_tma_multicast, smem_size = get_best_configs(m, n, k, 1, num_sms,
                                                                                   is_grouped_contiguous=True)
     args = (lhs, lhs_scales, rhs, rhs_scales, out,
             m_indices, m, num_groups,
@@ -158,7 +158,7 @@ def m_grouped_gemm_fp8_fp8_bf16_nt_masked(lhs: Tuple[torch.Tensor, torch.Tensor]
     # Auto-tuning with compilation
     global includes, template
     num_sms = get_num_sms()
-    block_m, block_n, num_stages, num_tma_multicast, smem_size = get_best_configs(expected_m, n, k, num_groups, num_sms)
+    num_sms, block_m, block_n, num_stages, num_tma_multicast, smem_size = get_best_configs(expected_m, n, k, num_groups, num_sms)
 
     # Extra checks for TMA store
     if num_groups > 1 and m > block_m: