diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp
index ee1f8f1bf..5fb2cb603 100644
--- a/examples/batched/batched.cpp
+++ b/examples/batched/batched.cpp
@@ -78,7 +78,7 @@ int main(int argc, char ** argv) {
     llama_context_params ctx_params = llama_context_default_params();
 
     ctx_params.seed  = 1234;
-    ctx_params.n_ctx = n_kv_req;
+    ctx_params.n_ctx   = n_kv_req;
     ctx_params.n_batch = std::max(n_len, n_parallel);
     ctx_params.n_seq_max       = n_parallel;
     ctx_params.n_threads       = params.n_threads;
diff --git a/ggml-metal.m b/ggml-metal.m
index 109e5fe6b..e9598ddff 100644
--- a/ggml-metal.m
+++ b/ggml-metal.m
@@ -1383,7 +1383,7 @@ static enum ggml_status ggml_metal_graph_compute(
                             !ggml_is_transposed(src0) &&
                             !ggml_is_transposed(src1) &&
                             src1t == GGML_TYPE_F32 &&
-                            ne00 % 32 == 0 && ne00 >= 64 &&
+                            ne00 % 32 == 0 && ne00 >= 128 &&
                             (ne11 > ne11_mm_min || (ggml_is_quantized(src0t) && ne12 > 1))) {
                             //printf("matrix: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12);
 
@@ -1698,7 +1698,7 @@ static enum ggml_status ggml_metal_graph_compute(
                         //       indirect matrix multiplication
                         // !!!
                         if ([ctx->device supportsFamily:MTLGPUFamilyApple7] &&
-                            ne20 % 32 == 0 && ne20 >= 64 &&
+                            ne20 % 32 == 0 && ne20 >= 128 &&
                             ne11 > ne11_mm_min) {
 
                             id<MTLComputePipelineState> pipeline = nil;
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index c2916c3e4..1998e1cbc 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -2091,6 +2091,13 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
         }
     }
 
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32,  64, 2,  128, { 8,  1}, {1, 1}));
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32,  83, 2,  128, { 8,  1}, {4, 1}));
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32,  64, 2,   64, { 8,  1}, {4, 1}));
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32,  83, 2,   64, { 8,  1}, {4, 1}));
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32,  64, 45, 128, { 8,  1}, {4, 1}));
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 128, 45,  64, { 8,  1}, {4, 1}));
+
     for (ggml_type type_a : all_types) {
         for (ggml_type type_b : {GGML_TYPE_F32 /*, GGML_TYPE_F16 */}) {
             for (int n_mats : {2, 4, 8}) {