Accelerating Inference: MLX vs. GGUF Models Compared

Running large language models locally has become increasingly practical, driven by improvements in quantization and hardware-aware optimization. As models grow in size and complexity, the choice of model format and runtime plays a central role in determining performance, memory usage, and deployment flexibility.

On Apple silicon, two options come up again and again: GGUF and MLX. Both aim to make large models usable on consumer hardware, and each takes a different route to get there. Knowing where those routes diverge is what makes the MLX vs GGUF choice tractable for a given workload.

GGUF: A portable format for quantized models

GGUF is a file format for storing models that GGML-based runtimes load, and llama.cpp is the runtime most practitioners meet first. A GGUF file holds the weights together with the metadata a runtime needs to load them, which is what lets one file travel between operating systems and hardware.

• Structured quantization. Weights are stored at reduced precision, commonly 4-bit, 5-bit or 8-bit.
• Broad compatibility. llama.cpp runs GGUF on CPUs and GPUs across Linux, Windows and macOS.
• Compact footprint. Lower precision lets larger models fit in limited memory.

The flexibility is the point. One model file can usually move between environments with little change, which keeps experimentation and deployment pipelines simple.

MLX: A framework built for Apple silicon

MLX starts from a different premise. It is an array framework for machine learning on Apple silicon, built by Apple machine learning research, and language models reach it through the mlx-lm package, which covers both text generation and fine-tuning. The design targets one hardware family instead of many.

That premise builds on the Apple M-series design, where a single pool of unified memory is shared across the CPU, the GPU and the Neural Engine.

• Hardware-aware execution. Kernels are written for the M-series GPU and dispatched through Metal.
• Group-wise quantization. Weights are quantized in small groups, each carrying its own scale and offset.
• Unified memory. CPU and GPU address the same pool, so tensors need no copy across a device bus.

An MLX build is a conversion of the original weights into MLX’s own quantized representation, distributed as safetensors rather than as a GGUF file. Its default 4-bit setting quantizes weights in groups of 64, each group storing a scale and an offset alongside the packed values. How much throughput that yields on a given machine depends on how well the runtime’s kernels exploit the layout.

Set side by side, the two are routinely treated as competing formats. The sections above show why the two sit at different levels. One is a file, the other is the machinery that runs it. Figure 1 lines the paths up level by level, from the stored weights down to the hardware they share.

Why performance differs

Where the two differ in throughput, the explanation usually lies in how each handles memory and computation.

Before comparing the mechanisms, it helps to see that inference is not a single speed. A run loads the model, processes the prompt, then generates tokens one at a time. Prompt processing is limited by compute and sets the time to first token. Token generation is limited by memory bandwidth and sets the streaming speed that a tokens-per-second figure reports. Figure 2 shows these phases, and how a prompt-heavy or generation-heavy workload shifts the balance between them.

Memory access patterns

On Apple silicon, both paths can place model weights in the same unified memory and run on the GPU through Metal, so neither depends on copying data across a separate device bus. The difference comes from the compute kernels and the quantization layout. MLX uses kernels and a group-wise quantization scheme written for the M-series GPU, which can improve how efficiently the available memory bandwidth is used during inference.

Compute frameworks

MLX dispatches work to the GPU through Metal, using kernels designed for the M-series GPU. The llama.cpp engine also has a Metal backend on Apple silicon, and it additionally supports CPU execution and other backends such as CUDA on non-Apple hardware. The practical difference is how much of each kernel is specialized for a single GPU family.

Quantization and bandwidth

For a quantized model generating one stream of text, memory bandwidth is usually the limiting factor rather than raw compute. Every new token reads the model weights out of memory, so the bandwidth of the system sets an upper bound on tokens per second. Both runtimes read from the same unified memory on Apple silicon, which feeds both the same bandwidth, but each build has its own ceiling, because that ceiling is the bandwidth divided by the size of that build. How close a run gets to its own ceiling depends on the efficiency of its kernels. Fewer bits per weight shrink the model and lift the ceiling, which is one reason 4-bit quantization is common for local work. Figure 3 shows the relationship.

The figure also carries a caution worth stating plainly. Equal bit width does not mean equal quantization. llama.cpp’s Q4_K_M mixture, the build usually labeled 4-bit medium, keeps some tensors at 6-bit, while MLX’s default 4-bit setting applies uniformly across groups of 64 weights. Two files can both be called 4-bit and still differ in size and in output quality.

Example: Phi-4-mini-reasoning on an M4 Max

One comparison makes the difference concrete. A GGUF build and an MLX build of Microsoft’s Phi-4-mini-reasoning, a reasoning model of 3.8 billion parameters, were each run once in LM Studio on a MacBook Pro with an M4 Max chip and 128 GB of unified memory. Both were 4-bit builds and both received the same prompt.

What was measured

Figures 4 and 5 show the two runs as the application reported them. The statistics line under each response carries the numbers the rest of this section works from.