Voxtral-Mini-3B-2507-TurboQuant-MLX-8bit

8-bit MLX weight-quantized build of mistralai/Voxtral-Mini-3B-2507 with a TurboQuant-compatible KV-cache profile. Tuned for Apple Silicon (M-series) inference via mlx-lm / mlx.

Hardware compatibility

Device	VRAM / RAM	Recommendation
Apple M4 Max 128 GB	~3.9 GB	recommended — headroom for long context
Apple M3 Max 64 GB	~3.9 GB	comfortable
Apple M2 Max 32 GB	~3.6 GB	fits

Overview

Base: mistralai/Voxtral-Mini-3B-2507 — 3B speech-understanding model
Capabilities: transcription, speech translation, audio QA
Weight precision: 8-bit (group-wise)
KV-cache profile: TurboQuant (per-head static calibration)
Approx. on-disk size: ~3 GB
Runtime: MLX on Apple Silicon (Unified Memory)

Quickstart

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("majentik/Voxtral-Mini-3B-2507-TurboQuant-MLX-8bit")

# Voxtral ingests audio; pass audio features through the processor pipeline
prompt = tokenizer.apply_chat_template(
    [{"role": "user", "content": [{"type": "audio", "path": "sample.wav"},
                                  {"type": "text", "text": "Transcribe this."}]}],
    add_generation_prompt=True,
)
text = generate(model, tokenizer, prompt=prompt, max_tokens=256)
print(text)

Model specs

Field	Value
Parameters	3B
Weight bits	8
Group size	64
Cache profile	TurboQuant
Size on disk	~3 GB
Target hardware	Apple Silicon (M1/M2/M3/M4)
License	Apache 2.0

RotorQuant vs TurboQuant

	TurboQuant	RotorQuant
Strategy	Per-head static calibration	Rotational online re-basis
Memory reduction	~3.5x on KV-cache	~4x on KV-cache
Best for	Batch transcription	Streaming / code-switching

TurboQuant-MLX-8bit is the highest-fidelity MLX build — closest WER to the reference model. Drop to 4-bit or 2-bit for smaller memory footprints.

Model tree for majentik/Voxtral-Mini-3B-2507-TurboQuant-MLX-8bit

Base model

mistralai/Voxtral-Mini-3B-2507

Quantized

(21)

this model

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Voxtral-Mini-3B-2507-TurboQuant-MLX-8bit