Yuval Avidani
Author
"Same model, same Mac, two engines, two different worlds"
Turns out you can run the exact same language model, on the exact same Mac, in two completely different ways, and each one feels totally different. We sat on this seriously in our repo called apple-silicon-arena, ran a real benchmark on an M-chip, and walked away without a sweeping "winner" but with a much clearer picture. So let's break this down slowly, because these two names, llama.cpp and MLX, show up everywhere these days, and most people don't actually know what the difference between them is.
Before anything else, one word that keeps coming back throughout this piece: inference. Inference is simply running a model that's already been trained to get an answer out of it. Think of it like driving a car that's already been built at the factory: you're not reassembling it, just hitting the gas and driving. Training, on the other hand, is building the car from scratch at the factory: taking a ton of data and calibrating the model until it knows how to work. That difference, between driving and building, is basically the central axis of this whole comparison.
What is llama.cpp, and why the whole world is built on it
Let's start with a clean definition: llama.cpp is an inference engine written in C/C++ that runs language models efficiently on your CPU or GPU, for anyone who wants to run a model locally without the cloud and without being tied to one platform. It was originally written by a developer named ggerganov, and has since become the infrastructure that half the local-AI world sits on.
Here's the fun part: we're probably already using it without knowing it. If you've ever run Ollama or LM Studio, the two most popular tools for running models on your computer, you've run llama.cpp under the hood. It's the engine hiding underneath.
Two technical terms worth knowing here. The first is GGUF, a file format used to store a model ready for running. Think of it like an MP3 file for models: one standard format that every "player" knows how to open. Thanks to this format, there are today thousands of ready-to-download models that just work. The second term is quantization, a way to shrink a model so it takes up less memory and runs faster, at the cost of a bit of accuracy. It's like compressing an image: it's a little less sharp, but suddenly it fits into your computer's memory and runs smoothly.
And there's another critical point: llama.cpp runs everywhere. Mac, Windows, Linux, even phones. If you've got a fleet of different machines, llama.cpp is the common language they all speak. On the Mac it knows how to take advantage of Apple's GPU through a layer called Metal (that's Apple's way of letting software talk directly to the graphics card), but it wasn't built specifically for Apple — it was built to be a citizen of the world.
What is MLX, and the magic of unified memory
Now let's flip to the other side. MLX is Apple's framework for array computing and machine learning, designed specifically for Apple's M-chips, for anyone working on a Mac who wants to squeeze every last drop out of the hardware. Notice the difference in framing: llama.cpp is a focused inference engine; MLX is a broader framework that knows how to both infer and train.
What Apple brought to the table here is full exploitation of a feature called unified memory. Let me explain why this is such a big deal. On a regular computer with a separate graphics card, there are two kinds of memory: CPU memory and GPU memory. For them to work together, data constantly needs to be copied back and forth, and that's slow and expensive. On Apple's M-chips, the CPU and GPU share the same physical memory pool. No copying, no waste: everyone's sitting around the same table, eating off the same plate.
Think of it like two chefs in a tiny kitchen. In a normal kitchen each chef has their own counter, and they keep running back and forth to each other carrying bowls. At Apple, there's one big counter that both of them work on at the same time. That's exactly what MLX knows how to exploit to the fullest, because it was designed precisely for this.
Another advantage of MLX is its API, the way developers talk to it. It's Pythonic and comfortable, very similar to NumPy and PyTorch (the libraries every machine learning person already knows). If you want to both run a model and explore, tweak, or train something of your own, MLX feels like home.
The historical downside? MLX is built around Apple. It used to run only on M-chips, but since then an official CUDA backend was added, letting it also run on Linux with NVIDIA cards; what it still lacks is official support for Windows or Android. This is a design choice: Apple built it to squeeze its own hardware to the max, and today you can also run it in the cloud on NVIDIA. A different approach, not necessarily a better one.
So who won our test?
Now we get to the part everyone's waiting for, and I'm going to disappoint you on purpose: in my view there's no sweeping winner, and anyone selling you a clean-cut answer simply hasn't run both of these seriously. In our benchmark in the apple-silicon-arena repo, we saw that each one shines in its own situation, and any attempt to crown one champion misses the point.
Here's how we actually think about it in practice. If the goal is just to download a model and run it, if you care that it works on both your Mac and a Linux server in the cloud, and if you want access to thousands of ready-made models, go with llama.cpp without hesitation. The massive ecosystem and portability are a winning card. For "just run this for me, anywhere," llama.cpp is our default.
On the other hand, if you're sitting on a Mac, if you want native performance that squeezes unified memory to the last drop, and if you also want to run and train a small model or explore an idea, open up MLX. It was built exactly for this moment.
My take: it's a question of need, not ego
Bottom line, the dilemma between llama.cpp and MLX isn't a question of "which is better," it's a question of "what do we need right now." It's like asking whether a hammer is better than a screwdriver: depends whether you've got a nail or a screw in front of you.
We also need to put a caveat on the table: the local-AI world moves insanely fast. Performance numbers get stale within weeks, new versions ship constantly, and what was true in our test can change. So don't treat this as gospel truth, treat it as a map that helps you understand the terrain, and run your own tests on your own hardware. The best benchmark is always the one you run yourself, on your own machine, with your own needs.
And if I had to sum up this whole piece in one line: llama.cpp is the way to run anywhere with the largest selection of ready-made models; MLX is the way to squeeze every last drop of power out of your Mac. Both are free, both are open, and both run on your own computer without sending a single byte to the cloud.
So now it's your turn to think: what do you actually need — an engine that truly runs everywhere, or an engine that was born for the Mac and excels on it, but already knows how to run on NVIDIA in the cloud too?