MERaLiON-3-10B-RotorQuant-MLX-2bit

2-bit weight-quantized MLX version of MERaLiON/MERaLiON-3-10B-preview with RotorQuant KV-cache quantization. Optimized for Apple Silicon inference via the MLX framework.

MERaLiON-3-10B is a multimodal audio-language model built on a Gemma-2 decoder backbone, designed for speech-to-text and audio understanding tasks.

Approximate model size: ~3 GB

Model Specifications

Property	Value
Base Model	MERaLiON/MERaLiON-3-10B-preview
Parameters	~10 billion
Architecture	Multimodal audio-language (Gemma-2 decoder backbone)
Modality	Audio + text input, text output
License	See base model
Weight Quantization	2-bit (~3 GB)
KV-Cache Quantization	RotorQuant
Framework	MLX (Apple Silicon)

Quickstart

from mlx_lm import load, generate

model, tokenizer = load("majentik/MERaLiON-3-10B-RotorQuant-MLX-2bit")

prompt = "Transcribe the following audio:"
response = generate(model, tokenizer, prompt=prompt, max_tokens=512)
print(response)

What is RotorQuant?

RotorQuant is a rotation-based KV cache quantization method that applies learned Clifford algebra rotations before quantizing the key-value cache. Key results:

• 5.3x faster prefill compared to TurboQuant baseline
• 28% faster decode throughput
• Perplexity: 6.91 vs 7.07 for TurboQuant (lower is better)

Note: 2-bit weight quantization is aggressive and may result in some quality degradation compared to 4-bit or 8-bit variants. Recommended for memory-constrained environments where fitting the model is the priority.

KV-Cache Quantization Comparison

Method	Prefill Speed	Decode Speed	Memory Savings	Reference
TurboQuant	Baseline	Baseline	High	arXiv: 2504.19874
RotorQuant	5.3x faster	28% faster	High	GitHub

Memory Estimates (MERaLiON-3-10B)

Precision	Approximate Size	MLX Variant
FP16 (original)	~20 GB	--
8-bit quantized	~10 GB	RotorQuant-MLX-8bit
4-bit quantized	~5 GB	RotorQuant-MLX-4bit
2-bit quantized	~3 GB	This model

Hardware Requirements

This model requires approximately 3 GB of unified memory. Recommended hardware:

• Apple M1 (8 GB+)
• Any Apple Silicon Mac

See Also

• MERaLiON/MERaLiON-3-10B-preview -- Base model
• majentik/MERaLiON-3-10B-RotorQuant-MLX-8bit -- MLX 8-bit variant
• majentik/MERaLiON-3-10B-RotorQuant-MLX-4bit -- MLX 4-bit variant
• majentik/MERaLiON-3-10B-TurboQuant-MLX-2bit -- TurboQuant MLX 2-bit variant
• RotorQuant GitHub
• MLX Framework

Quant trade-off (MLX lane)

Bits	Approx size	Use case	Recommendation
2-bit	~2.6 GB	Aggressive quantization	Very low-RAM Macs
3-bit	~3.6 GB	Lossy but small	Low-RAM Macs
4-bit	~4.2 GB	Balanced default	Recommended for most Macs
5-bit	~5.0 GB	Higher fidelity	Quality-sensitive
6-bit	~6.0 GB	Approaching FP16 quality	High-fidelity
8-bit	~7.6 GB	Near-lossless reference	Fidelity-critical work

(Current variant — 2bit — is bolded.)

Variants in this family

(Showing 8 sibling variants under majentik/meralion3-10b-*. The current variant — RotorQuant-MLX-2bit — is bolded.)

Variant	Runtime	Approx size	Use case
RotorQuant	runtime modifier	n/a	KV-cache root (weight-agnostic)
RotorQuant-MLX-2bit	mlx-lm	~3.2 GB	Apple Silicon, smallest
RotorQuant-MLX-4bit	mlx-lm	~6.2 GB	Apple Silicon balanced
RotorQuant-MLX-8bit	mlx-lm	~12 GB	Apple Silicon reference
TurboQuant	runtime modifier	n/a	KV-cache root (weight-agnostic)
TurboQuant-MLX-2bit	mlx-lm	~3.2 GB	Apple Silicon, smallest
TurboQuant-MLX-4bit	mlx-lm	~6.2 GB	Apple Silicon balanced
TurboQuant-MLX-8bit	mlx-lm	~12 GB	Apple Silicon reference