---
base_model: openai/gpt-oss-20b
library_name: transformers
tags:
  - rotorquant
  - kv-cache-quantization
  - gpt-oss
  - openai
  - moe
  - quantized
license: apache-2.0
pipeline_tag: text-generation
---

# GPT-OSS-20B - RotorQuant KV Cache

**RotorQuant KV-cache quantization** applied to [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b). RotorQuant uses block-diagonal rotations (Clifford algebra) to compress the KV cache, delivering 5.3x faster prefill and 28% faster decode compared to TurboQuant with equivalent memory savings.

This repository provides the RotorQuant KV-cache configuration for GPT-OSS-20B, OpenAI's first open-weights release in years (Apache 2.0). The model weights remain at their original precision; only the key-value cache is quantized at runtime. GPT-OSS-20B is a Mixture-of-Experts model that rivals o3-mini on reasoning benchmarks and is ideal for local and edge deployment.

## Model Specifications

| Property | Value |
|---|---|
| **Base Model** | [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) |
| **Parameters** | 20 billion (MoE) |
| **Architecture** | Mixture-of-Experts (MoE) Transformer |
| **License** | Apache 2.0 (commercial use OK) |
| **Quantization** | RotorQuant KV-cache only (weights unchanged) |
| **Downloads** | 6M+ on HuggingFace |

## Quickstart

```python
from rotorquant import IsoQuantCache
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "openai/gpt-oss-20b"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

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

inputs = tokenizer("Explain the theory of relativity.", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, past_key_values=cache)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## What is RotorQuant?

[RotorQuant](https://github.com/scrya-com/rotorquant) applies block-diagonal rotations (Clifford algebra) for KV cache compression. It provides equivalent memory savings to TurboQuant while dramatically improving throughput.

Key advantages over TurboQuant:
- **5.3x faster prefill**
- **28% faster decode**
- Equivalent memory savings
- Slightly better perplexity

## 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 (GPT-OSS-20B)

| Precision | Approximate Size |
|---|---|
| BF16 (original) | ~40 GB |
| 8-bit quantized | ~20 GB |
| 4-bit quantized | ~12 GB |
| 2-bit quantized | ~6 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

- [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) -- Base model
- [majentik/gpt-oss-20b-TurboQuant](https://huggingface.co/majentik/gpt-oss-20b-TurboQuant) -- TurboQuant KV-cache variant
- [majentik/gpt-oss-20b-RotorQuant-MLX-8bit](https://huggingface.co/majentik/gpt-oss-20b-RotorQuant-MLX-8bit) -- MLX 8-bit variant
- [majentik/gpt-oss-20b-RotorQuant-MLX-4bit](https://huggingface.co/majentik/gpt-oss-20b-RotorQuant-MLX-4bit) -- MLX 4-bit variant
- [majentik/gpt-oss-20b-RotorQuant-MLX-2bit](https://huggingface.co/majentik/gpt-oss-20b-RotorQuant-MLX-2bit) -- MLX 2-bit variant
- [majentik/gpt-oss-20b-RotorQuant-GGUF-Q4_K_M](https://huggingface.co/majentik/gpt-oss-20b-RotorQuant-GGUF-Q4_K_M) -- GGUF Q4_K_M variant
- [RotorQuant GitHub](https://github.com/scrya-com/rotorquant)