diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 08383edb40..8ed875f9ef 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -2142,6 +2142,26 @@ static bool ggml_cuda_should_fuse_mul_mat_vec_f(const ggml_tensor * tensor) {
     return use_mul_mat_vec_f;
 }
 
+static bool ggml_cuda_should_use_mmvq(ggml_type type, int cc, int64_t ncols_dst) {
+    if (ncols_dst > MMVQ_MAX_BATCH_SIZE) {
+        return false;
+    }
+
+    if (GGML_CUDA_CC_IS_RDNA4(cc)) {
+        switch (type) {
+            case GGML_TYPE_IQ2_S:
+            case GGML_TYPE_IQ2_XXS:
+            case GGML_TYPE_IQ3_XXS:
+            case GGML_TYPE_IQ3_S:
+                return ncols_dst <= 4;
+            default:
+                break;
+        }
+    }
+
+    return true;
+}
+
 static bool ggml_cuda_should_fuse_mul_mat_vec_q(const ggml_tensor * tensor) {
     ggml_tensor *       src0 = tensor->src[0];
     ggml_tensor *       src1 = tensor->src[1];
@@ -2151,11 +2171,11 @@ static bool ggml_cuda_should_fuse_mul_mat_vec_q(const ggml_tensor * tensor) {
                                    ggml_nbytes(src0) != ggml_backend_buffer_get_alloc_size(src0->buffer, src0) &&
                                    src0->view_src;
 
+    const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
     bool use_mul_mat_vec_q = ggml_is_quantized(src0->type) && !bad_padding_clear && src1->type == GGML_TYPE_F32 &&
-                             dst->type == GGML_TYPE_F32 && src1->ne[1] <= MMVQ_MAX_BATCH_SIZE;
+                             dst->type == GGML_TYPE_F32 && ggml_cuda_should_use_mmvq(src0->type, cc, src1->ne[1]);
 
     // fusion is not universally faster on Pascal
-    const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
     if (cc <= GGML_CUDA_CC_PASCAL) {
         return false;
     }
@@ -2212,6 +2232,7 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
 
             const int cc            = ggml_cuda_info().devices[id].cc;
             const int warp_size     = ggml_cuda_info().devices[id].warp_size;
+            use_mul_mat_vec_q       = use_mul_mat_vec_q         && ggml_cuda_should_use_mmvq(src0->type, cc, src1->ne[1]);
             use_mul_mat_q           = use_mul_mat_q             && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1], /*n_experts=*/0);
             use_mul_mat_f           = use_mul_mat_f             && ggml_cuda_should_use_mmf(src0->type, cc, warp_size, src0->ne, src0->nb, src1->ne[1], /*mul_mat_id=*/false);
             use_mul_mat_vec_f       = use_mul_mat_vec_f         && ggml_cuda_should_use_mmvf(src0->type, cc, src0->ne, src0->nb, src1->ne[1]);
@@ -2220,6 +2241,7 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
     } else {
         const int cc            = ggml_cuda_info().devices[ctx.device].cc;
         const int warp_size     = ggml_cuda_info().devices[ctx.device].warp_size;
+        use_mul_mat_vec_q       = use_mul_mat_vec_q         && ggml_cuda_should_use_mmvq(src0->type, cc, src1->ne[1]);
         use_mul_mat_q           = use_mul_mat_q             && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1], /*n_experts=*/0);
         use_mul_mat_f           = use_mul_mat_f             && ggml_cuda_should_use_mmf(src0->type, cc, warp_size, src0->ne, src0->nb, src1->ne[1], /*mul_mat_id=*/false);
         use_mul_mat_vec_f       = use_mul_mat_vec_f         && ggml_cuda_should_use_mmvf(src0->type, cc, src0->ne, src0->nb, src1->ne[1]);
diff --git a/ggml/src/ggml-cuda/mmq.cu b/ggml/src/ggml-cuda/mmq.cu
index 9a69f41d15..fa39790504 100644
--- a/ggml/src/ggml-cuda/mmq.cu
+++ b/ggml/src/ggml-cuda/mmq.cu
@@ -356,8 +356,42 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11, int64_t
             }
         }
 
-        // For RDNA4 MMQ is consistently faster than dequantization + hipBLAS:
-        // https://github.com/ggml-org/llama.cpp/pull/18537#issuecomment-3706422301
+        if (GGML_CUDA_CC_IS_RDNA4(cc)){
+            // if (n_experts >= 64) {
+            //     return true;
+            // }
+            switch (type) {
+                case GGML_TYPE_IQ2_S:
+                case GGML_TYPE_Q6_K:
+                    return ne11 <= 128;
+                case GGML_TYPE_Q4_0:
+                case GGML_TYPE_Q4_1:
+                case GGML_TYPE_Q5_0:
+                case GGML_TYPE_Q5_1:
+                case GGML_TYPE_MXFP4:
+                    return true;
+                case GGML_TYPE_Q5_K:
+                case GGML_TYPE_IQ3_XXS:
+                case GGML_TYPE_IQ3_S:
+                case GGML_TYPE_IQ2_XS:
+                case GGML_TYPE_IQ2_XXS:
+                case GGML_TYPE_Q2_K:
+                case GGML_TYPE_Q3_K:
+                case GGML_TYPE_IQ1_S:
+                case GGML_TYPE_Q4_K:
+                    return ne11 <= 256;
+                case GGML_TYPE_Q8_0:
+                case GGML_TYPE_IQ4_NL:
+                case GGML_TYPE_IQ4_XS:
+                    return ne11 <= 512;
+
+                default:
+                    return false;
+
+            }
+
+            return false;
+        }
         return true;
     }