File size: 3,995 Bytes
79a981b 446dc48 79a981b 446dc48 79a981b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 | ---
base_model: google/gemma-4-31B
library_name: mlx
tags:
- rotorquant
- kv-cache-quantization
- gemma
- gemma4
- multimodal
- quantized
- mlx
- 4bit
license: apache-2.0
pipeline_tag: image-text-to-text
language:
- en
---
# Gemma 4 31B - RotorQuant MLX 4-bit
**4-bit weight-quantized MLX version** of [google/gemma-4-31B](https://huggingface.co/google/gemma-4-31B) with RotorQuant KV-cache quantization. Optimized for Apple Silicon inference via the [MLX](https://github.com/ml-explore/mlx) framework. RotorQuant delivers 5.3x faster prefill and 28% faster decode compared to TurboQuant.
Approximate model size: **~17 GB**
## Model Specifications
| Property | Value |
|---|---|
| **Base Model** | [google/gemma-4-31B](https://huggingface.co/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** | 4-bit (~17 GB) |
| **KV-Cache Quantization** | RotorQuant |
| **Framework** | MLX (Apple Silicon) |
## Quickstart
```python
import mlx.core as mx
from mlx_lm import load, generate
model, tokenizer = load("majentik/gemma-4-31B-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:
```python
from mlx_vlm import load, generate
model, processor = load("majentik/gemma-4-31B-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](https://github.com/scrya-com/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: aggressively smaller model weights for reduced disk and memory footprint, plus 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](https://arxiv.org/abs/2504.19874) |
| **RotorQuant** | **5.3x faster** | **28% faster** | High | [GitHub](https://github.com/scrya-com/rotorquant) |
## Memory Estimates (Gemma 4 31B)
| Precision | Approximate Size | MLX Variant |
|---|---|---|
| FP16 (original) | ~62 GB | -- |
| 8-bit quantized | ~31 GB | [RotorQuant-MLX-8bit](https://huggingface.co/majentik/gemma-4-31B-RotorQuant-MLX-8bit) |
| **4-bit quantized** | **~17 GB** | **This model** |
| 2-bit quantized | ~9 GB | [RotorQuant-MLX-2bit](https://huggingface.co/majentik/gemma-4-31B-RotorQuant-MLX-2bit) |
## Hardware Requirements
This model requires approximately 17 GB of unified memory. Recommended hardware:
- Apple M1 Pro (32 GB+)
- Apple M2 Pro (32 GB+)
- Apple M3 Pro (36 GB+)
- Apple M4 Pro (24 GB+)
- Any Apple Silicon Mac with 24 GB+ unified memory
## See Also
- [google/gemma-4-31B](https://huggingface.co/google/gemma-4-31B) -- Base model
- [majentik/gemma-4-31B-RotorQuant](https://huggingface.co/majentik/gemma-4-31B-RotorQuant) -- RotorQuant KV-cache only (transformers)
- [majentik/gemma-4-31B-RotorQuant-MLX-8bit](https://huggingface.co/majentik/gemma-4-31B-RotorQuant-MLX-8bit) -- MLX 8-bit variant
- [majentik/gemma-4-31B-RotorQuant-MLX-2bit](https://huggingface.co/majentik/gemma-4-31B-RotorQuant-MLX-2bit) -- MLX 2-bit variant
- [majentik/gemma-4-31B-TurboQuant-MLX-4bit](https://huggingface.co/majentik/gemma-4-31B-TurboQuant-MLX-4bit) -- TurboQuant MLX 4-bit variant
- [RotorQuant GitHub](https://github.com/scrya-com/rotorquant)
- [MLX Framework](https://github.com/ml-explore/mlx)
|