[!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), 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.

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

8-bit MLX weight-quantized build of 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

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

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.

majentik/Voxtral-Mini-4B-Realtime-2602-TurboQuant-MLX-8bit