---
base_model: openai/gpt-oss-20b
library_name: mlx
tags:
  - rotorquant
  - kv-cache-quantization
  - gpt-oss
  - openai
  - moe
  - quantized
  - mlx
  - 2bit
license: apache-2.0
pipeline_tag: text-generation
---

# GPT-OSS-20B - RotorQuant MLX 2-bit

**2-bit weight-quantized MLX version** of [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) with RotorQuant KV-cache quantization. Optimized for Apple Silicon inference via the [MLX](https://github.com/ml-explore/mlx) framework. The smallest variant with RotorQuant's superior KV-cache throughput -- ideal for memory-constrained devices. GPT-OSS-20B is OpenAI's first open-weights release in years (Apache 2.0), a Mixture-of-Experts model that rivals o3-mini on reasoning benchmarks.

Approximate model size: **~6 GB**

## 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) |
| **Weight Quantization** | 2-bit (~6 GB) |
| **KV-Cache Quantization** | RotorQuant |
| **Framework** | MLX (Apple Silicon) |

## Quickstart

```python
from mlx_lm import load, generate
from rotorquant import IsoQuantCache

model, tokenizer = load("majentik/gpt-oss-20b-RotorQuant-MLX-2bit")

prompt = "Explain the theory of relativity."
response = generate(model, tokenizer, prompt=prompt, max_tokens=512)
print(response)
```

## What is RotorQuant?

[RotorQuant](https://github.com/scrya-com/rotorquant) applies block-diagonal rotations (Clifford algebra) for KV cache compression. Combined with aggressive 2-bit weight quantization in MLX, this produces the smallest possible footprint for GPT-OSS-20B while retaining RotorQuant's fast KV-cache throughput.

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 (GPT-OSS-20B)

| Precision | Approximate Size | MLX Variant |
|---|---|---|
| BF16 (original) | ~40 GB | -- |
| 8-bit quantized | ~20 GB | [RotorQuant-MLX-8bit](https://huggingface.co/majentik/gpt-oss-20b-RotorQuant-MLX-8bit) |
| 4-bit quantized | ~12 GB | [RotorQuant-MLX-4bit](https://huggingface.co/majentik/gpt-oss-20b-RotorQuant-MLX-4bit) |
| **2-bit quantized** | **~6 GB** | **This model** |

## Hardware Requirements

This model requires approximately 6 GB of unified memory. Recommended hardware:
- Apple M1 (8 GB+)
- Apple M2 (8 GB+)
- Apple M3 (8 GB+)
- Apple M4 (8 GB+)
- Any Apple Silicon Mac with 8 GB+ unified memory

## See Also

- [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) -- Base model
- [majentik/gpt-oss-20b-RotorQuant](https://huggingface.co/majentik/gpt-oss-20b-RotorQuant) -- RotorQuant KV-cache only (transformers)
- [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-TurboQuant-MLX-2bit](https://huggingface.co/majentik/gpt-oss-20b-TurboQuant-MLX-2bit) -- TurboQuant MLX 2-bit variant
- [RotorQuant GitHub](https://github.com/scrya-com/rotorquant)
- [MLX Framework](https://github.com/ml-explore/mlx)