---
library_name: gguf
base_model: google/gemma-4-31B
tags:
  - gguf
  - rotorquant
  - kv-cache-quantization
  - gemma
  - gemma4
  - dense
  - multimodal
  - llama-cpp
  - quantized
license: apache-2.0
---

# gemma-4-31B-RotorQuant-GGUF-Q4_K_M

GGUF Q4_K_M weight-quantized variant of [google/gemma-4-31B](https://huggingface.co/google/gemma-4-31B) with **RotorQuant** KV cache compression for efficient inference with llama.cpp, Ollama, and LM Studio.

## Overview

This model combines two compression techniques:
- **GGUF Q4_K_M weight quantization** — reduces model size from ~62GB to ~18 GB
- **RotorQuant KV cache compression** — block-diagonal rotations (Clifford algebra) for 3-bit KV cache, 5.3x faster prefill

## Quickstart

### llama.cpp
```bash
llama-cli -m gemma-4-31B-RotorQuant-GGUF-Q4_K_M.gguf \
  --cache-type-k planar3 --cache-type-v iso3 \
  -p "Explain quantum computing"
```

### Ollama
```bash
ollama run majentik/gemma-4-31B-RotorQuant-GGUF-Q4_K_M
```

### LM Studio
Download the GGUF file and load in LM Studio. Enable RotorQuant KV cache in advanced settings.

## Specifications

| Property | Value |
|----------|-------|
| Base Model | google/gemma-4-31B |
| Parameters | 31B dense |
| Weight Quantization | GGUF Q4_K_M |
| KV Cache | RotorQuant 3-bit (planar/iso) |
| File Size | ~18 GB |
| License | Apache 2.0 |
| Compatible | llama.cpp, Ollama, LM Studio, koboldcpp |

## What is RotorQuant?

RotorQuant applies block-diagonal rotations (Clifford algebra) for KV cache compression. When used with llama.cpp's `--cache-type-k planar3 --cache-type-v iso3` flags:

| Metric | RotorQuant | TurboQuant |
|--------|-----------|-----------|
| Prefill Speed | 3,822 tok/s | 722 tok/s |
| Decode Speed | 119 tok/s | 93 tok/s |
| Perplexity | 6.91 | 7.07 |

## See Also

- [RotorQuant GitHub](https://github.com/scrya-com/rotorquant)
- [Base model](https://huggingface.co/google/gemma-4-31B)
- [MLX variants](https://huggingface.co/majentik/gemma-4-31B-RotorQuant-MLX-4bit)