majentik/gemma-4-E2B-it-RotorQuant-MLX-4bit

Gemma 4 E2B-it - RotorQuant MLX 4-bit

4-bit weight-quantized MLX version of google/gemma-4-E2B-it 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. A good balance between model quality and memory efficiency.

Approximate model size: ~1.2 GB

Model Specifications

Property	Value
Base Model	google/gemma-4-E2B-it
Parameters	~2 billion
Architecture	Dense transformer
Modality	Multimodal: image + text input, text output
License	Apache 2.0
Weight Quantization	4-bit (~1.2 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-E2B-it-RotorQuant-MLX-4bit")

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-E2B-it-RotorQuant-MLX-4bit")

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 4-bit weight quantization in MLX, this provides a dual compression strategy with superior KV-cache performance: smaller model weights plus faster compressed KV cache for efficient long-context generation.

Key advantages over TurboQuant:

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

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 E2B-it)

Precision	Approximate Size	MLX Variant
FP16 (original)	~4 GB	--
8-bit quantized	~2 GB	RotorQuant-MLX-8bit
4-bit quantized	~1.2 GB	This model
2-bit quantized	~0.6 GB	RotorQuant-MLX-2bit

Hardware Requirements

This model requires approximately 1.2 GB of unified memory. Recommended hardware:

• Apple M1 (8 GB+)
• Apple M2 (8 GB+)
• Apple M3 (8 GB+)
• Apple M4 (8 GB+)
• Any Apple Silicon Mac with 8 GB+ unified memory

See Also

• google/gemma-4-E2B-it -- Base model
• majentik/gemma-4-E2B-it-RotorQuant -- RotorQuant KV-cache only (transformers)
• majentik/gemma-4-E2B-it-RotorQuant-MLX-8bit -- MLX 8-bit variant
• majentik/gemma-4-E2B-it-RotorQuant-MLX-2bit -- MLX 2-bit variant
• majentik/gemma-4-E2B-it-TurboQuant-MLX-4bit -- TurboQuant MLX 4-bit variant
• RotorQuant GitHub
• MLX Framework