The autoregressive language model of MiniMaxAI/MiniMax-Music3, converted to MLX and quantized, plus the bridge code that plugs it back into the original diffusers pipeline.

Upstream states that inference requires CUDA and that CPU and MPS are not supported. On a 24 GB Apple Silicon machine the bf16 pipeline does not merely run slowly, it never reaches generation: 21.6 GB of weights plus activations plus the OS puts the machine into full swap. This repo removes that wall by replacing the single component responsible for 80% of the weight footprint.

This is a memory port, not a speed port. Read the benchmarks before you decide whether it is useful to you.

The other components (flow-matching transformer, RVQ depth decoder, Flow-VAE vocoder, condition encoder) are not redistributed here. They are used unmodified in bf16 from the upstream repo.

The pipeline touches Qwen3ForCausalLM at exactly three points:

That is a small enough surface to reimplement on top of an MLX model, which is what mlx_bridge.py does. Everything else stays in torch/MPS, so the numerical path of the diffusion stage is untouched.

Peak process footprint during a 30 s generation: 15.7 GB, no swap.

5.264 bits per weight overall. The two embedding tables are kept at 8 bits because every audio code the model emits is looked up and scored through them, and they are only 1.6 GB of the total at that width.

Apple M4 Pro, 24 GB unified memory, macOS 25.6, torch 2.13, MLX 0.32. 30 s of audio, 30 denoising steps, measured with explicit torch.mps.synchronize().

Component load takes 10.5 s. Generation of 30 s of audio takes 356 to 413 s across runs, which is 12x to 14x slower than realtime. A 90 s track takes roughly 20 minutes.

Where that time goes, per stage:

Per-operation costs behind those totals:

The language model is not the bottleneck once quantized: MLX accounts for about 6% of wall time. The flow-matching transformer dominates, running 2 forwards per denoising step because classifier-free guidance is executed as two separate batch-1 passes. Batching them into one batch-2 forward was measured and gains 1.1%, so that stage is compute-bound rather than launch-bound. Making this fast on Apple Silicon means porting the flow transformer to MLX, which this repo does not do. Second target after that would be the RVQ depth decoder, whose 7 sequential steps per audio frame cost 3x more than the language model they feed.

Measure with torch.mps.synchronize() around anything you time here. Torch is asynchronous on MPS, and without explicit syncs the cost of the denoising loop is attributed to whichever later call happens to drain the queue. An earlier version of this profile reported 219 s in the condition encoder, a 25M-parameter Conv1d whose real cost is 0.3 s.

If you have a 24 GB CUDA GPU, use the upstream repo instead. It will be far faster.

You can skip downloading language_model/* in step 1 if you only ever use the MLX path. It is listed above because --lm torch falls back to it.

modular_model_index.json points every component at the Hub repo id rather than at your local directory, so the pipeline re-downloads 28 GB even though the weights are already on disk. generate.py rewrites those paths on startup.

The decoder returns (channels, samples); soundfile expects (samples, channels). Writing the array as-is fails with Format not recognised.

Unchanged from upstream. The caption drives the sound, the lyrics only get sung. The official music-caption-rewriter skill in the MiniMax-AI/MiniMax-Music3 GitHub repo carries a genre router and 1000 reference captions, and is worth reading before writing prompts by hand.

No formal evaluation was run against the bf16 reference, because the bf16 reference does not fit on the hardware this port targets. Spectral checks on 30 s outputs are consistent with music rather than noise (13.9% of energy below 200 Hz, 34.6% in 200 Hz to 2 kHz, 17 dB crest factor, structured RMS envelope). Treat that as a sanity check, not as a quality claim. If you have a machine that can run both, an A/B would be a genuinely useful contribution.

MiniMax-Music3 Community License, reproduced unchanged in LICENSE. The copyright notice must travel with any copy. Commercial use requires displaying "MiniMax-Music3" in the product interface, and separate written authorization from MiniMax above 20M USD yearly revenue.

Upstream components carry their own lineage: the language model derives from Qwen3-8B (Apache 2.0), the DiT from Stable Audio Open, the VAE from a modified Stability AI design. See the upstream LICENSE for the full chain.

All credit for the model belongs to MiniMax. This repo contributes a quantization and a compatibility shim, nothing more.