diff --git a/ggml/src/ggml-cuda/fattn-new-mma.cu b/ggml/src/ggml-cuda/fattn-new-mma.cu
index 5a6869a5..4d6556dd 100644
--- a/ggml/src/ggml-cuda/fattn-new-mma.cu
+++ b/ggml/src/ggml-cuda/fattn-new-mma.cu
@@ -135,7 +135,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_load_tile(
     // K/V data is loaded with decreasing granularity for D for better memory bandwidth.
     // The minimum granularity with cp.async is 16 bytes, with synchronous data loading it's 4 bytes.
 
-    if (use_cp_async) {
+    if constexpr (use_cp_async) {
         const unsigned int tile_KV_32 = __cvta_generic_to_shared(tile_KV);
 
         constexpr int preload = 64;
@@ -205,7 +205,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_load_mask(
         const half2 * const __restrict__ mask_h2, half2 * const __restrict__ tile_mask, const int stride_mask) {
     static_assert(nbatch_fa == 2*WARP_SIZE || WARP_SIZE % nbatch_fa == 0, "bad KQ_per_iter");
 
-    if (use_cp_async) {
+    if constexpr (use_cp_async) {
         constexpr int preload = nbatch_fa >= 32 ? nbatch_fa * sizeof(half) : 64;
         constexpr int cols_per_warp = 8*WARP_SIZE/nbatch_fa;
         constexpr int stride_j = nwarps * cols_per_warp;