majentik/gemma-4-31B-RotorQuant-MLX-2bit

Gemma 4 31B - RotorQuant MLX 2-bit

2-bit weight-quantized MLX version of google/gemma-4-31B with RotorQuant KV-cache quantization. Optimized for Apple Silicon inference via the MLX framework. RotorQuant delivers 5.3x faster prefill and 28% faster decode compared to TurboQuant. The most aggressive quantization, fitting the full 31B model in under 10 GB.

Approximate model size: ~9 GB

Model Specifications

Property	Value
Base Model	google/gemma-4-31B
Parameters	31 billion (dense transformer)
Architecture	Dense transformer (not MoE)
Modality	Multimodal: image + text input, text output
License	Apache 2.0
Weight Quantization	2-bit (~9 GB)
KV-Cache Quantization	RotorQuant
Framework	MLX (Apple Silicon)

Quickstart

import mlx.core as mx
from mlx_lm import load, generate

model, tokenizer = load("majentik/gemma-4-31B-RotorQuant-MLX-2bit")

prompt = "Describe this image in detail."
response = generate(model, tokenizer, prompt=prompt, max_tokens=512)
print(response)

For multimodal usage with images:

from mlx_vlm import load, generate

model, processor = load("majentik/gemma-4-31B-RotorQuant-MLX-2bit")

prompt = "What do you see in this image?"
output = generate(model, processor, prompt=prompt, image="path/to/image.jpg", max_tokens=512)
print(output)

What is RotorQuant?

RotorQuant is a high-performance KV-cache quantization method that achieves significantly better throughput than TurboQuant. Combined with 2-bit weight quantization in MLX, this provides maximum compression with the best available KV-cache performance: the smallest possible model footprint plus the fastest compressed KV cache for efficient long-context generation.

Key advantages over TurboQuant:

• 5.3x faster prefill
• 28% faster decode
• Equivalent memory savings

Note: 2-bit quantization is the most aggressive option and may result in some quality degradation compared to higher-precision variants. It is best suited for experimentation, rapid prototyping, or hardware-constrained environments.

KV-Cache Quantization Comparison

Method	Prefill Speed	Decode Speed	Memory Savings	Reference
TurboQuant	1x (baseline)	1x (baseline)	High	arXiv: 2504.19874
RotorQuant	5.3x faster	28% faster	High	GitHub

Memory Estimates (Gemma 4 31B)

Precision	Approximate Size	MLX Variant
FP16 (original)	~62 GB	--
8-bit quantized	~31 GB	RotorQuant-MLX-8bit
4-bit quantized	~17 GB	RotorQuant-MLX-4bit
2-bit quantized	~9 GB	This model

Hardware Requirements

This model requires approximately 9 GB of unified memory. Recommended hardware:

• Apple M1 (16 GB+)
• Apple M2 (16 GB+)
• Apple M3 (16 GB+)
• Apple M4 (16 GB+)
• Any Apple Silicon Mac with 16 GB+ unified memory

See Also

• google/gemma-4-31B -- Base model
• majentik/gemma-4-31B-RotorQuant -- RotorQuant KV-cache only (transformers)
• majentik/gemma-4-31B-RotorQuant-MLX-8bit -- MLX 8-bit variant
• majentik/gemma-4-31B-RotorQuant-MLX-4bit -- MLX 4-bit variant
• majentik/gemma-4-31B-TurboQuant-MLX-2bit -- TurboQuant MLX 2-bit variant
• RotorQuant GitHub
• MLX Framework