When getting started with local AI on a Mac or iOS device, two common inference ecosystems you will encounter are MLX and llama.cpp. And when running AI locally on your Apple device, one of the first choices you will make is whether you choose a model prepared for MLX or a GGUF model designed for a llama.cpp-based runtime. But what does this all mean? What significance does this have for the model you use and its performance?
The naming conventions in the local AI space are a little confusing, to say the least, so let's break down these terms a bit.
Llama is the name of Meta's model family. llama.cpp, despite its similar name, is an independent piece of open-source software that started as a lightweight C/C++ inference engine for running Meta's LLaMA-family models locally. Now, llama.cpp supports many model architectures stored in the GGUF format, including Qwen, Gemma, Mistral, Phi, and others.
GGUF is the model file format that is usually associated with llama.cpp. GGUF models are packaged into a single file with a .gguf extension. For a typical text model, the one file contains all of the model weights and metadata needed for running it.
MLX is a machine-learning framework designed specifically for Apple silicon. MLX does not have a specific model format. Instead, a model prepared for MLX is commonly distributed as a folder containing one or more Safetensors weight files, a configuration file, and tokenizer files. Apple provides several open-source projects built around MLX. Arbiter uses Apple's official mlx-swift-lm package, which is built on top of mlx-swift, Apple's lower-level Swift interface to the MLX framework.
One of the biggest benefits of the llama.cpp and GGUF architecture is portability. Because llama.cpp runs across many platforms, the same GGUF model can be easily shared across macOS, Windows, Linux, and iOS, given that the application supports the model's architecture and quantization type. This makes GGUF useful when you want to move models between devices or use the same model with several different applications.
MLX is limited to Apple silicon hardware, but that is what ultimately gives it some key advantages. Apple silicon is built around a unified memory architecture. In simple terms, unified memory allows the GPU and CPU to access the same RAM. Many consumer PCs have substantially more system RAM than dedicated GPU memory. Because Apple silicon uses one shared pool, a Mac with 32 GB or 64 GB of unified memory can potentially run models that would not fit entirely within the VRAM of a typical consumer graphics card. However, memory bandwidth and GPU compute performance also matter, so it's not a given that a Mac will necessarily be better for local AI inference. MLX is built to fully take advantage of this pooled memory, which can result in faster performance and more efficient memory use.
This does not mean that every MLX model will automatically outperform its GGUF equivalent. llama.cpp can also be optimized for Apple hardware and supports Metal acceleration. Actual performance depends on a wide variety of factors, including the parameter count, context length, quantization method, and device specs.
It is also important to separate the model format from quantization. Both MLX-prepared models and GGUF models can be distributed at different quantization levels. Original model checkpoints are often released using higher-precision representations like FP16 or BF16. Quantization converts the model's weights into more compact representations, commonly described as 8-bit, 6-bit, 4-bit, or lower. The quantization techniques get pretty complicated, and we would need several more articles devoted to explaining that process. In short, the process of quantization reduces memory usage and can improve inference speed, but it can also negatively affect model output quality. Thus, two versions of the same model may behave differently depending on the quantization technique used.
Multimodal support is another area to consider. Both the MLX and llama.cpp ecosystems support a growing number of models that can process images along with text. That being said, in my research for Arbiter, particularly when looking at smaller models suitable for iPhones, I found more usable multimodal options in the MLX ecosystem.
For most Arbiter users on Apple silicon, I recommend starting with an MLX version when a well-supported conversion of the model is available. MLX is designed specifically for Apple hardware and may offer better performance or efficiency for certain models.
GGUF remains an excellent option when you want broader model availability, cross-platform portability, simpler single-file model management, or access to a particular quantization that is not available through MLX.
Neither option is universally better. The right choice depends on your device, the particular model, the available conversions, and whether you value Apple-specific optimization or broader portability. For Arbiter, I personally recommend MLX models for devices that can support them. It's the more Apple-native way of using local models. But as always, the space moves too fast to form opinions that are too strong. Stay tuned for the latest updates, as the space is constantly evolving.