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README.md

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+---
+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)