diff --git a/ggml/src/ggml-cuda/fattn-common.cuh b/ggml/src/ggml-cuda/fattn-common.cuh
index a19491821..41d948335 100644
--- a/ggml/src/ggml-cuda/fattn-common.cuh
+++ b/ggml/src/ggml-cuda/fattn-common.cuh
@@ -981,7 +981,7 @@ template <int DV, int ncols1, int ncols2>
 void launch_fattn(
     ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kernel_t fattn_kernel, const int nwarps, const size_t nbytes_shared,
     const int nbatch_fa, const bool need_f16_K, const bool need_f16_V, const bool stream_k, const bool use_sparse,
-    const int warp_size = WARP_SIZE
+    const int warp_size = WARP_SIZE, const bool async_kv_preload = false
 ) {
     constexpr int ncols = ncols1 * ncols2;
 
@@ -1114,7 +1114,8 @@ void launch_fattn(
     // Optional optimization where the mask is scanned to determine whether part of the calculation can be skipped.
     // Only worth the overhead if there is at lease one FATTN_KQ_STRIDE x FATTN_KQ_STRIDE square to be skipped or
     //     multiple sequences of possibly different lengths.
-    if (!use_sparse && mask && K->ne[1] % FATTN_KQ_STRIDE == 0 && (Q->ne[1] >= 1024 || Q->ne[3] > 1)) {
+    const bool scan_mask = !use_sparse && mask && K->ne[1] % FATTN_KQ_STRIDE == 0 && (Q->ne[1] >= 1024 || Q->ne[3] > 1);
+    if (scan_mask) {
         const int64_t s31 = mask->nb[1] / sizeof(half2);
         const int64_t s33 = mask->nb[3] / sizeof(half2);
 
@@ -1142,10 +1143,16 @@ void launch_fattn(
 
     dim3 blocks_num;
     if (stream_k) {
-        auto should_use_stream_k = [](const int cc, const int ntiles_dst, const int max_blocks, const int DKQ) {
+        // Stream-K splits the work before the mask scan is applied, so skipped KV tiles make the blocks uneven.
+        const bool prefer_whole_tiles = GGML_CUDA_CC_IS_NVIDIA(cc) && cc == GGML_CUDA_CC_DGX_SPARK && async_kv_preload && scan_mask;
+
+        auto should_use_stream_k = [prefer_whole_tiles](const int cc, const int ntiles_dst, const int max_blocks, const int DKQ) {
             const int tiles_nwaves             = (ntiles_dst + max_blocks - 1) / max_blocks;
             const int tiles_efficiency_percent = 100 * ntiles_dst / (max_blocks*tiles_nwaves);
 
+            if (prefer_whole_tiles && tiles_efficiency_percent >= 75) {
+                return false;
+            }
             if (GGML_CUDA_CC_IS_NVIDIA(cc) && cc >= GGML_CUDA_CC_ADA_LOVELACE) {
                 return true;
             }
diff --git a/ggml/src/ggml-cuda/fattn-mma-f16.cuh b/ggml/src/ggml-cuda/fattn-mma-f16.cuh
index 9d221d23e..7f0f6abfd 100644
--- a/ggml/src/ggml-cuda/fattn-mma-f16.cuh
+++ b/ggml/src/ggml-cuda/fattn-mma-f16.cuh
@@ -2100,8 +2100,9 @@ void ggml_cuda_flash_attn_ext_mma_f16_case(ggml_backend_cuda_context & ctx, ggml
         }
     }
 
+    const bool async_kv_preload = nstages == 2 && !use_sparse;
     launch_fattn<DV, ncols1, ncols2>
-        (ctx, dst, fattn_kernel, nwarps, nbytes_shared_total, nbatch_fa, true, true, true, use_sparse, warp_size_host);
+        (ctx, dst, fattn_kernel, nwarps, nbytes_shared_total, nbatch_fa, true, true, true, use_sparse, warp_size_host, async_kv_preload);
 }