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There is no proven overall winner among MLX’s affine, mxfp4, and nvfp4 4-bit modes. The mlx-lm converter defaults to affine, but that default does not show it performs worse—or better—than the alternatives. The right choice depends on your model, task, Apple silicon device, and priorities for output quality, memory, speed, and compatibility.
What 4-bit formats does the MLX converter offer?
The mlx-lm converter accepts four quantization modes: affine, mxfp4, nvfp4, and mxfp8. The last is an 8-bit mode, not a 4-bit option. The converter source on the moving main branch lists these defaults; they may change, so check the version you are using. mlx-lm converter implementation.
| Mode | Default bit width | Default group size |
|---|---|---|
| affine | 4 | 64 |
| mxfp4 | 4 | 32 |
| nvfp4 | 4 | 16 |
| mxfp8 | 8 | 32 |
The CLI also exposes --q-bits and --q-group-size, so the defaults are configurable. A different default group size means these modes are not necessarily a like-for-like comparison when left at their defaults.
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No conclusion about quality, speed, or memory follows from the default alone. The implementation establishes that affine is selected by default and that the converter offers alternatives; it does not establish that affine is intentionally disadvantaged or that another mode is universally better. The official sources cited here do not provide a controlled head-to-head benchmark proving an overall winner.
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Why “4-bit” does not tell the whole storage story
Nominal bit width is not identical to effective storage per weight. The Hugging Face transformers-to-MLX guide estimates that 4-bit quantization with group size 64 typically uses about 4.5 effective bits per weight once scale and bias metadata are included. This is a rough estimate from that guide, not a universal measured figure for every mode or model. Hugging Face Transformers-to-MLX guide.
For that reason, compare converted artifact size and peak memory rather than assuming that every format labeled 4-bit produces the same total footprint.
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Could mixed precision be a better fit?
Uniform 4-bit conversion is not the only option. mlx-lm includes mixed-bit recipes that assign more bits to selected components, including value projections, down projections, and the language-model head, while leaving other quantizable layers at fewer bits. The exact allocation depends on the recipe.
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How to decide which mode is best for your workload
Choose a comparison that matches what you need the model to do. Keep the model, device, runtime, and workload consistent; otherwise, a measured difference may reflect something other than the quantization mode.
- Define the task and quality measure. Use a stated evaluation set or repeatable task-specific prompts, with a scoring method you can apply to every candidate.
- Compare storage and memory. Record converted artifact size and peak memory, accounting for metadata overhead rather than relying on nominal bits alone.
- Measure speed under the same conditions. Keep runtime, prompt length, generation length, and device the same for each run.
- Check compatibility. Verify that the model architecture and runtime support the selected mode, then confirm that the converted model loads and behaves as intended.
- Document bit allocation. Distinguish a uniform 4-bit conversion from a mixed-bit recipe. Their labels do not mean every layer has the same precision.
A defensible winner is the mode—or layer allocation—that performs best against your stated priorities in this controlled comparison. Without measurements for a named model, device, software version, and workload, the available evidence does not identify one.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Where MLX fits
MLX LM is intended for language-model work on Apple silicon, and Apple’s developer material demonstrates model conversion and quantization. The cited material does not establish that a particular Mac model or memory configuration is required. Apple’s WWDC25 MLX LM demonstration.
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