---
base_model: mistralai/Voxtral-Mini-4B-Realtime-2602
library_name: mlx
license: apache-2.0
pipeline_tag: automatic-speech-recognition
tags:
  - voxtral
  - audio
  - speech
  - speech-recognition
  - realtime
  - streaming
  - asr
  - mlx
  - turboquant
  - quantization
  - 8-bit
---

> [!TIP]
> **KV-cache quantization without any fork (recommended, 2026):** upstream
> llama.cpp/Ollama now cover this natively — use `-ctk q8_0 -ctv q8_0`
> (~half KV memory, negligible quality loss: perplexity +0.002–0.05) or
> `-ctk q4_0 -ctv q4_0` (~quarter memory, ≈7.6% perplexity increase). In
> Ollama: `OLLAMA_KV_CACHE_TYPE=q8_0` with `OLLAMA_FLASH_ATTENTION=1`. Keep
> K and V types symmetric to stay on the fast fused Flash-Attention path.
> Since April 2026, mainline llama.cpp also applies Hadamard rotation to
> KV activations ([PR #21038](https://github.com/ggml-org/llama.cpp/pull/21038)),
> which greatly improves low-bit KV quality (opt-out:
> `LLAMA_ATTN_ROT_DISABLE=1`).
>
> The RotorQuant/TurboQuant fork flow below is **experimental/legacy**: the
> TurboQuant llama.cpp PR was closed without merging (June 2026) and the fork
> is unmaintained relative to mainline. It is NOT required to use this model.
<!-- kv-upstream-note -->

# Voxtral-Mini-4B-Realtime-2602-TurboQuant-MLX-8bit

8-bit MLX weight-quantized build of [`mistralai/Voxtral-Mini-4B-Realtime-2602`](https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-2602) with a TurboQuant KV-cache profile. Highest-fidelity MLX variant for low-latency streaming ASR on Apple Silicon.

## Hardware compatibility

| Device | VRAM / RAM | Recommendation |
| --- | --- | --- |
| Apple M4 Max 128 GB | ~5.2 GB | recommended — headroom for long context |
| Apple M3 Max 64 GB | ~5.2 GB | comfortable |
| Apple M2 Max 32 GB | ~4.8 GB | fits |

## Overview

- **Base:** `mistralai/Voxtral-Mini-4B-Realtime-2602` — 4B real-time ASR model
- **Weight precision:** 8-bit (group-wise)
- **KV-cache profile:** TurboQuant
- **Approx. on-disk size:** ~4 GB
- **Runtime:** MLX on Apple Silicon

## Quickstart

```bash
pip install mlx-lm
```

```python
from mlx_lm import load, generate

model, tokenizer = load("majentik/Voxtral-Mini-4B-Realtime-2602-TurboQuant-MLX-8bit")

for chunk in audio_stream():
    prompt = tokenizer.apply_chat_template(
        [{"role": "user", "content": [{"type": "audio", "path": chunk}]}],
        add_generation_prompt=True,
    )
    emit(generate(model, tokenizer, prompt=prompt, max_tokens=32))
```

## Model specs

| Field | Value |
|---|---|
| Parameters | 4B |
| Weight bits | 8 |
| Group size | 64 |
| Cache profile | TurboQuant |
| Size on disk | ~4 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 | Predictable domains, lowest p50 latency | Noisy/multi-speaker streams |

## See also

- [`majentik/Voxtral-Mini-4B-Realtime-2602-TurboQuant-MLX-4bit`](https://huggingface.co/majentik/Voxtral-Mini-4B-Realtime-2602-TurboQuant-MLX-4bit)
- [`majentik/Voxtral-Mini-4B-Realtime-2602-TurboQuant-MLX-2bit`](https://huggingface.co/majentik/Voxtral-Mini-4B-Realtime-2602-TurboQuant-MLX-2bit)
- [`majentik/Voxtral-Mini-4B-Realtime-2602-RotorQuant-MLX-8bit`](https://huggingface.co/majentik/Voxtral-Mini-4B-Realtime-2602-RotorQuant-MLX-8bit)
- [`mistralai/Voxtral-Mini-4B-Realtime-2602`](https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-2602) — upstream base model

## About the RotorQuant / TurboQuant labels

RotorQuant and TurboQuant are this project's **release labels**, not distinct
quantization algorithms — for any given tier, both brand repos carry
byte-identical weights produced with the standard MLX / llama.cpp quantizers.
No brand-specific speedup is claimed or measured.