llama.cpp vs MLX
llama.cpp — the engine most other tools wrap. Widest format and hardware support, and where new architectures land first. MLX — apple's own array framework. The native path on Apple Silicon, with its own model format. They share no model format, so switching means downloading again.
Side by side
| llama.cpp | MLX | |
|---|---|---|
| Model formats | GGUF | MLX (safetensors-based) |
| KV cache quantization | yes | yes |
| CPU offload | yes | no |
| MoE expert offload | yes | no |
| Multi-GPU | Layer split by default — capacity adds up, bandwidth does not. Row split available. | Not applicable — one chip, one unified memory pool. |
| Concurrency | Single-user focused. A server exists but is not built for heavy concurrency. | Single-user focused. |
| Platforms | Linux, macOS, Windows | macOS |
Choose llama.cpp if…
Anyone who wants the newest architectures, the most quantization choices, or the most control over memory.
It is a command-line tool with a lot of flags. The defaults have improved considerably — it now sizes offload automatically — but it expects you to know what you are asking for.
Choose MLX if…
Apple Silicon, particularly at larger model sizes where the unified memory pool is the whole reason you bought the machine.
Its models are a separate format, so a GGUF you already downloaded will not work. Model availability is narrower than GGUF, though the popular families are all converted.
Why there is no speed comparison here
We have not benchmarked these against each other, so we will not rank them on speed. Most of them wrap the same engine, which makes the differences that matter capability rather than throughput — and where a real speed gap exists it usually comes from configuration, such as how much of the model fits on the GPU and whether the cache is quantized, rather than from the runtime itself.