llama.cpp Metal Tensor Parallel closes MLX speed gap for Mac inference
Mac users running local LLMs are weighing whether llama.cpp's new Metal Tensor Parallel support closes the performance gap with MLX, the Apple Silicon-native framework that has dominated local inference since 2023.
Mac users running local LLMs are weighing whether llama.cpp's new Metal Tensor Parallel (MTP) support closes the performance gap with MLX, the Apple Silicon-native framework that has dominated local inference since 2023. MTP splits model layers across multiple GPU cores, a capability MLX has lacked in most production builds. The question now is whether GGUF quantized models running on llama.cpp with MTP can match or beat MLX's raw token generation speed, especially given MLX's reputation for tighter Metal API integration.
The MLX ecosystem remains fragmented on MTP support. LM Studio's MLX backend lacks both MTP and reliable prompt caching. omlx ships TurboQuant and dflash but no MTP in stable releases, though the feature sits in the dev branch. Two newer wrappers—rapid-mlx and mtplx—are circulating, with mtplx claiming MTP support, but neither has the configuration depth of llama.cpp. That breadth matters: GGUF quantization offers more bit-width and layout options than MLX's native format, and llama.cpp's Metal backend now supports the same tensor-parallel dispatch that made MLX fast in the first place.
No public benchmarks yet compare MTP-enabled llama.cpp against MLX on identical hardware and model sizes. The practical test is prompt processing and token generation speed on a MacBook Pro M3 Max or M4—the two chips where MTP's multi-core split would show gains. Until someone runs that head-to-head, Mac users are choosing between MLX's cleaner API and llama.cpp's wider quantization menu, now with the same parallel dispatch under the hood.