diff --git a/ggml/src/ggml-openvino/ggml-openvino.cpp b/ggml/src/ggml-openvino/ggml-openvino.cpp
index 1a3fca08f7e..5372fd275cf 100644
--- a/ggml/src/ggml-openvino/ggml-openvino.cpp
+++ b/ggml/src/ggml-openvino/ggml-openvino.cpp
@@ -1223,11 +1223,8 @@ static ggml_openvino_op_support is_op_supported_case(const ggml_tensor * op) {
         if (ggml_openvino_get_device_name() == "GPU" && op->src[0] != nullptr && op->src[0]->type == GGML_TYPE_BF16) {
             return {false, "MUL_MAT_ID with BF16 weights on GPU is not supported"};
         }
-        // GPU MUL_MAT_ID uses a Gather+MatMul fallback because the GPU plugin rejects internal
-        // GatherMatmul for these test shapes. Skip cases that would materialize a large selected
-        // expert-weight temporary.
-        if (ggml_openvino_get_device_name() == "GPU" && mul_mat_id_requires_large_tmp(op)) {
-            return {false, "MUL_MAT_ID requires large temporary on GPU"};
+        if (op->src[0] != nullptr && op->src[0]->type == GGML_TYPE_MXFP4 && mul_mat_id_requires_large_tmp(op)) {
+            return {false, "MUL_MAT_ID with MXFP4 weights requires large temporary"};
         }
         break;
     }
diff --git a/ggml/src/ggml-openvino/openvino/op/mul_mat_id.cpp b/ggml/src/ggml-openvino/openvino/op/mul_mat_id.cpp
index f1b28c85d40..0de6161bed8 100644
--- a/ggml/src/ggml-openvino/openvino/op/mul_mat_id.cpp
+++ b/ggml/src/ggml-openvino/openvino/op/mul_mat_id.cpp
@@ -56,54 +56,6 @@ ov::Output<ov::Node> static_shape_dims_or_shapeof(const ov::Output<ov::Node> & i
     return get_dimensions(shape, dims);
 }
 
-ov::Output<ov::Node> translate_mul_mat_id_gather_matmul_fallback(const NodeContext & context,
-                                                                 ov::Output<ov::Node> expert_weights,
-                                                                 ov::Output<ov::Node> activations,
-                                                                 ov::Output<ov::Node> ids) {
-    auto gather_axis = ov::op::v0::Constant::create(ov::element::i32, ov::Shape{}, {0});
-    ov::Output<ov::Node> selected_weights = std::make_shared<ov::op::v8::Gather>(expert_weights, ids, gather_axis);
-
-    const auto output_type = context.get_output_type();
-    if (selected_weights.get_element_type() != ov::element::f32) {
-        selected_weights = std::make_shared<ov::op::v0::Convert>(selected_weights, ov::element::f32);
-    }
-    if (activations.get_element_type() != ov::element::f32) {
-        activations = std::make_shared<ov::op::v0::Convert>(activations, ov::element::f32);
-    }
-
-    auto activations_shape = std::make_shared<ov::op::v3::ShapeOf>(activations, ov::element::i64);
-    auto ids_shape = std::make_shared<ov::op::v3::ShapeOf>(ids, ov::element::i64);
-    ov::Output<ov::Node> acts_target_dims = std::make_shared<ov::op::v0::Concat>(
-        ov::OutputVector{
-            get_dimensions(activations_shape, {0}),
-            get_dimensions(ids_shape, {1}),
-            get_dimensions(activations_shape, {2}),
-        },
-        0);
-    ov::Output<ov::Node> acts_broadcasted =
-        std::make_shared<ov::op::v3::Broadcast>(activations, acts_target_dims, ov::op::BroadcastType::BIDIRECTIONAL);
-
-    auto activations_expanded = std::make_shared<ov::op::v0::Unsqueeze>(acts_broadcasted, const_i64({2}));
-    ov::Output<ov::Node> result =
-        std::make_shared<ov::op::v0::MatMul>(activations_expanded, selected_weights, false, true);
-
-    auto output_shape = context.get_output_shape();
-    FRONT_END_OP_CONVERSION_CHECK(output_shape.rank().is_static() && output_shape.rank().get_length() == 4,
-                                  "Unexpected MUL_MAT_ID output rank");
-    FRONT_END_OP_CONVERSION_CHECK(output_shape[3].is_static(), "Expected static row dimension for MUL_MAT_ID output");
-
-    auto batch_dim = ov::op::v0::Constant::create(ov::element::i64, {1}, {1});
-    auto row_dim = ov::op::v0::Constant::create(ov::element::i64, {1}, {output_shape[3].get_length()});
-    auto result_target_dims = std::make_shared<ov::op::v0::Concat>(
-        ov::OutputVector{batch_dim, get_dimensions(ids_shape, {0, 1}), row_dim}, 0);
-    result = std::make_shared<ov::op::v1::Reshape>(result, result_target_dims, false);
-
-    if (result.get_element_type() != output_type) {
-        result = std::make_shared<ov::op::v0::Convert>(result, output_type);
-    }
-    return result;
-}
-
 ov::Output<ov::Node> translate_mul_mat_id_mxfp4_packed(const NodeContext & context,
                                                        ov::Output<ov::Node> expert_weights,
                                                        ov::Output<ov::Node> activations,
@@ -229,7 +181,6 @@ OutputVector translate_mul_mat_id(const NodeContext & context) {
     auto expert_weights_rank = expert_weights.get_partial_shape().rank();
     FRONT_END_OP_CONVERSION_CHECK(expert_weights_rank.is_static(),
                                   "Expected static rank for MUL_MAT_ID expert weights");
-    const bool use_gpu_fallback = ggml_openvino_get_device_name() == "GPU";
     if (expert_weights_rank.get_length() == 4) {
         auto expert_weights_shape_3d = static_shape_dims_or_shapeof(expert_weights, {1, 2, 3});
         expert_weights = std::make_shared<ov::op::v1::Reshape>(expert_weights, expert_weights_shape_3d, false);
@@ -246,14 +197,9 @@ OutputVector translate_mul_mat_id(const NodeContext & context) {
     }
 
     const auto output_type = context.get_output_type();
-    if (activations.get_element_type() != ov::element::f32) {
-        activations = std::make_shared<ov::op::v0::Convert>(activations, ov::element::f32);
-    }
-
-    if (use_gpu_fallback || !expert_weights.get_partial_shape().is_static() || !activations.get_partial_shape().is_static() ||
-        !ids.get_partial_shape().is_static()) {
-        return rename_outputs_with_suffix({translate_mul_mat_id_gather_matmul_fallback(context, expert_weights, activations, ids)},
-                                          context.get_name());
+    const auto activations_type = ggml_openvino_get_device_name() == "GPU" ? ov::element::f16 : ov::element::f32;
+    if (activations.get_element_type() != activations_type) {
+        activations = std::make_shared<ov::op::v0::Convert>(activations, activations_type);
     }
 
     // GatherMatmul's A input is [n_used_or_1, n_tokens, k]; activations_3d is