Executable programs and extensions

GGUF vs MLX: which model format should you download?

The model name is the same, but one side is a GGUF file and the other side is an MLX folder. Can I get a smaller one? To reduce re-download time, you should decide which app to run before the file. Both deployment methods are choices for using the same source in different execution environments.

A matching model name does not guarantee compatibility

The program must understand not only the weights, but also the model structure and tokenization scheme. GGUF is a widely used distribution format in the llama.cpp family. It contains weights, metadata, etc., but is not an all-purpose file that automatically supports all new structures.

The distribution for MLX uses model and configuration files tailored to Apple Silicon's MLX execution environment. Strictly speaking, we are comparing a container called GGUF and an execution ecosystem called MLX. Just changing the file extension does not change them.

Illustration of the same original model converted to GGUF and MLX formats and passing through different execution paths. — The file format is not just an extension, but also determines the quantization method, runtime features, and optimization paths that can be used.

First, see what path the current app uses

If you plan to use llama.cpp series apps on Windows or Linux, it is natural to look for a supported GGUF. If you use an MLX-based app on your Mac, first check the MLX distribution recommended by the app. The GGUF path can also be used on Macs, so it is not necessarily determined by the operating system alone.

Although apps on the same screen offer both paths, the actual engines and supported options may differ. Don't just look at the file size, look at the features and runtime version you want to use. If you haven't received the model yet, this confirmation will save you from downloading large files again.

Illustration of highly portable model files spreading to multiple CPU/GPU adapters and dedicated formats leading directly to integrated memory chips — GGUF is easy to open on a variety of devices, while MLX is good at utilizing Apple Silicon's dedicated path.

The designation Q4 is also independent of the format.

There can be multiple precisions in GGUF, and the MLX distribution also has multiple quantizations. Just because it's the same 4 bits doesn't mean the number is reduced in the same way. File size, quality, and execution speed cannot be considered to be completely the same.

When comparing two paths, use the same original model, similar precision, and the same input length. Different chat templates may change results. If you leave only the two values, model name and tok/s, it will be difficult to find the reason for the difference later.

Illustration of two runtimes opening the same model file supporting different quantization, cache and MTP features. — Even if the weights are in the file, you won't be able to use those features if the runtime doesn't understand the new architecture and special heads.

If you can use text but not images or MTP,

The translation may be missing additional weights, or the executable may not yet be able to handle those functions. Being a general answer means confirming that one path. Vision input, tool calls and MTP must be viewed separately for scope of support.

If it is a split file, you will need to get all the pieces you need and may require a separate encoder. First, check normal operation by asking a text question before adding features. There is no need to start by deleting all healthy files to fix the problem.

How to choose files you will use for a long time

Rather than concluding that one of the two formats is always superior, it is more important to ensure that it opens reliably and uses the necessary features in the current app. After satisfying the conditions, compare the results of the usual questions with the first token, decode, and memory.

Once you find a good setup, take note of the file version and app version. There will be a standard to return to even after the update. Any choice that saves you time actually organizing documentation and fixing code rather than downloading models is enough.