These two get compared as if you pick one. You mostly do not — they sit at different layers, and the comparison only makes sense once you know which layer your problem is on.
Ollama is a packaged runner
Ollama's job is to make running a language model on your own machine a one-line affair. You pull a model, you talk to it, there is a local API on a port. It is built around llama.cpp and quantised weights, and the whole value proposition is that you do not think about any of that.
If what you want is a chat model, a coding assistant or an embedding endpoint running locally, Ollama is almost always the right answer and the comparison ends here.
MLX is a framework you build on
MLX is Apple's array framework for Apple Silicon — closer in spirit to NumPy or PyTorch than to a model runner. It does not ship you a chat interface. It gives you arrays, autodiff and a unified-memory model, and expects you to write or port the thing you actually want to run.
That sounds like a downside until the model you need is not a chat model. Video generation is the obvious case: there is no ollama pull for an LTX video pipeline. Somebody has to port the model to MLX, and then you are running on MLX whether you wanted to think about it or not.
The unified-memory part they share
Both benefit from the same Apple Silicon property: the GPU addresses the whole machine's memory. A 64GB Mac can hold a model that a 24GB discrete card cannot, without the offloading that turns nominally faster hardware into slower hardware.
Both also inherit the same sharp edge, and this one cost us a full sweep of renders. MLX allocations are invisible to every free-memory reading macOS exposes. vm_stat, memory pressure, Activity Monitor — all of them look healthy right up until the kernel kills the process with rc=137. A “check if there's RAM free” guard is not a guard; it is a coin flip.
The fix is to price the job rather than poll the machine: estimate what a given shape of work costs, and refuse to claim it unless this node can afford it. Our sidecar from a real 1080×1920, 97-frame render reads peak_mem_gb: 44.1 against a budget_gb: 57.6, and those are measured numbers, not a model of memory we talked ourselves into.
So which do you want
Running a language model locally: Ollama, and stop reading. Running something there is no package for — video, a custom port, anything where you need the weights in your own process: MLX, and budget for the fact that the specific port matters more than the silicon does.
They are not rivals. One of them is a product and the other is a dependency.
The caveat worth stating
Throughput on Apple Silicon is not close to a current CUDA card for this work, and we have never pretended otherwise — the honest comparison is here. Macs win on memory, cost and the model staying in the building. Plan for throughput across several machines, not latency on one.