---
base_model: google/gemma-4-31B
library_name: transformers
tags:
  - rotorquant
  - kv-cache-quantization
  - gemma
  - gemma4
  - multimodal
  - quantized
license: apache-2.0
pipeline_tag: image-text-to-text
---

# Gemma 4 31B - RotorQuant KV Cache

**RotorQuant KV-cache quantization** applied to [google/gemma-4-31B](https://huggingface.co/google/gemma-4-31B), delivering 5.3x faster prefill and 28% faster decode compared to TurboQuant while maintaining equivalent memory savings.

This repository provides the RotorQuant KV-cache configuration for Gemma 4 31B. The model weights remain at their original precision; only the key-value cache is quantized at runtime.

## 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 |
| **Quantization** | RotorQuant KV-cache only (weights unchanged) |

## Quickstart

```python
from rotorquant import RotorQuantCache
from transformers import AutoModelForImageTextToText, AutoProcessor

model_id = "google/gemma-4-31B"

processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(model_id, device_map="auto")

# Apply RotorQuant KV-cache quantization
cache = RotorQuantCache(model)

inputs = processor("Describe this image.", images=image, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, past_key_values=cache)
print(processor.decode(outputs[0], skip_special_tokens=True))
```

## What is RotorQuant?

[RotorQuant](https://github.com/scrya-com/rotorquant) is a high-performance KV-cache quantization method that builds on the foundations of cache compression while achieving significantly better throughput. It compresses the key-value cache used during autoregressive generation without modifying model weights.

Key benefits:
- **5.3x faster prefill** compared to TurboQuant
- **28% faster decode** compared to TurboQuant
- **No weight modification** -- model weights stay at original precision
- **Reduced inference memory** -- KV cache is compressed significantly
- **Longer context windows** -- fit more tokens in the same GPU memory

## 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 |
|---|---|
| FP16 (original) | ~62 GB |
| 8-bit quantized | ~31 GB |
| 4-bit quantized | ~17 GB |
| 2-bit quantized | ~9 GB |

Note: These estimates are for weight quantization. This repository applies KV-cache quantization only, so model weight memory remains at the precision you load the model in. The KV-cache memory savings are realized during generation.

## See Also

- [google/gemma-4-31B](https://huggingface.co/google/gemma-4-31B) -- Base model
- [majentik/gemma-4-31B-TurboQuant](https://huggingface.co/majentik/gemma-4-31B-TurboQuant) -- TurboQuant KV-cache variant
- [majentik/gemma-4-31B-RotorQuant-MLX-8bit](https://huggingface.co/majentik/gemma-4-31B-RotorQuant-MLX-8bit) -- MLX 8-bit weight-quantized variant
- [majentik/gemma-4-31B-RotorQuant-MLX-4bit](https://huggingface.co/majentik/gemma-4-31B-RotorQuant-MLX-4bit) -- MLX 4-bit weight-quantized variant
- [majentik/gemma-4-31B-RotorQuant-MLX-2bit](https://huggingface.co/majentik/gemma-4-31B-RotorQuant-MLX-2bit) -- MLX 2-bit weight-quantized variant
- [RotorQuant GitHub](https://github.com/scrya-com/rotorquant)