Ollama vs MLX
Ollama — llama.cpp wrapped in a daemon and a model registry. Convenient, at the cost of some control and some lag. 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
| Ollama | MLX | |
|---|---|---|
| Model formats | GGUF | MLX (safetensors-based) |
| KV cache quantization | yes | yes |
| CPU offload | yes | no |
| MoE expert offload | no | no |
| Multi-GPU | Inherits llama.cpp's layer split; little direct control. | Not applicable — one chip, one unified memory pool. |
| Concurrency | Fine for a handful of concurrent requests, not for a production workload. | Single-user focused. |
| Platforms | Linux, macOS, Windows | macOS |
Choose Ollama if…
Getting started, and any application that wants a local OpenAI-compatible endpoint without managing the engine.
Its short model names map to specific quantizations that are not obvious — a bare tag is usually a 4-bit build, not the model's best available. It also lags upstream, so a very new architecture may not load yet even though llama.cpp supports it.
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.