# Model Card: Intel/gpt-oss-20b-int4-g64-rtn-AutoRound

## Model Details

- **Model Name**: Intel/gpt-oss-20b-int4-g64-rtn-AutoRound
- **Developer**: Intel, based on OpenAI's gpt-oss-20b
- **Release Date**: Not explicitly stated in available information
- **Model Type**: Mixed INT4 language model with symmetric quantization
- **Base Model**: OpenAI/gpt-oss-20b
- **Quantization**: 4-bit integer (INT4) with group size 64, using Intel's AutoRound via Round-To-Nearest (RTN) without algorithm tuning
- **License**: Apache 2.0
- **Model Size**: Approximately 1.8 billion parameters (quantized)
- **Tensor Types**: I32, BF16, F16
- **Non-Expert Layers**: Fallback to 16-bit precision (BF16/F16)

This model is a quantized version of OpenAI's gpt-oss-20b, optimized for efficient inference on various hardware, including CPUs, Intel GPUs, and CUDA-enabled GPUs. It is designed for lower latency and specialized use cases, leveraging a Mixture-of-Experts (MoE) architecture with approximately 20 billion total parameters, of which about 3.6 billion are active per inference pass.[](https://huggingface.co/Intel/gpt-oss-20b-int4-g64-rtn-AutoRound)[](https://huggingface.co/Intel/gpt-oss-20b-int4-rtn-AutoRound/blob/main/README.md)

## Intended Use

- **Primary Use Cases**:
  - Local inference on consumer-grade hardware (e.g., desktops, laptops)
  - Specialized tasks requiring low-latency text generation
  - Research and experimentation in natural language processing
  - Agentic workflows with strong instruction following, tool use (e.g., web search, Python code execution), and reasoning capabilities
- **Supported Tasks**:
  - Text generation
  - Instruction following
  - Chain-of-thought reasoning
  - Structured outputs
- **Intended Users**:
  - Developers and researchers
  - Enterprises building AI applications
  - Hardware enthusiasts testing local inference performance

The model is suitable for scenarios requiring efficient deployment on resource-constrained devices, such as those with 16GB of memory. It supports a context window of up to 131,072 tokens, with a recommended minimum of 16,384 for reasoning tasks.[](https://huggingface.co/Intel/gpt-oss-20b-int4-g64-rtn-AutoRound)[](https://www.hardware-corner.net/guides/gpt-oss-20b-gpu-benchamrks/)

## How to Use

### Inference with Transformers
```python
from transformers import pipeline

model_id = "Intel/gpt-oss-20b-int4-g64-rtn-AutoRound"
pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True
)
messages = [{"role": "user", "content": "Explain quantum mechanics clearly and concisely."}]
outputs = pipe(messages, max_new_tokens=512)
print(outputs[0]["generated_text"][-1])
```

## Hardware Requirements

- **Minimum**: 16GB VRAM for local inference (e.g., NVIDIA RTX 3090)
- **Recommended**: Single 80GB GPU (e.g., NVIDIA H100, AMD MI300X) for optimal performance
- **Tested Platforms**:
  - Windows 11: Up to 36,000-token context with 24GB VRAM (RTX 3090)
  - Linux: Up to 52,000-token context with 24GB VRAM (RTX 3090)
- **Performance** (on RTX 3090, MXFP4 format):
  - Windows: ~24–36 tokens/second (t/s) generation at 2,000–36,000 token context
  - Linux: ~55–114 t/s generation at 2,000–50,000 token context

Linux setups typically offer better performance due to lower VRAM overhead.[](https://www.hardware-corner.net/guides/gpt-oss-20b-gpu-benchamrks/)

## Ethical Considerations and Limitations

- **Limitations**:
  - The model may produce factually incorrect outputs and should not be relied upon for factual accuracy without verification.
  - Potential for generating biased, lewd, or offensive content due to limitations in the pretrained model and fine-tuning datasets.
  - Quantization may slightly degrade performance compared to the full-precision model.
- **Ethical Considerations**:
  - Developers should perform safety testing before deployment to mitigate risks of harmful outputs.
  - Users should be informed of the model’s limitations and potential biases.
  - The model’s open-weight nature allows fine-tuning, which could be misused to bypass safety mechanisms.

Consult legal advice before using the model for commercial purposes.[](https://huggingface.co/Intel/gpt-oss-20b-int4-AutoRound)

## Training and Quantization Details

- **Base Model**: OpenAI/gpt-oss-20b, a Mixture-of-Experts model with 20 billion total parameters (~3.6 billion active per inference).
- **Quantization Method**: Intel’s AutoRound with RTN (no algorithm tuning), using group size 64 and symmetric quantization for INT4 precision.
- **Weight Precision**:
  - MoE projection weights: MXFP4 (4.25 bits per parameter)
  - Non-expert layers: BF16/F16 (16-bit)
- **Training Data**: Not disclosed in available information.
- **Quantization Benefits**: Reduces memory footprint, enabling deployment on systems with as little as 16GB of memory.

The model leverages Intel’s Neural Compressor for optimization. For more details, see Intel’s documentation.[](https://huggingface.co/Intel/gpt-oss-20b-int4-AutoRound)[](https://huggingface.co/Intel/gpt-oss-20b-int4-rtn-AutoRound/blob/main/README.md)

## Evaluation

- **Performance Metrics**: The model has been tested for inference speed on consumer hardware (e.g., RTX 3090), showing competitive token generation rates (see Hardware Requirements).
- **Safety Evaluations**: Based on OpenAI’s evaluations of gpt-oss-20b, the model does not reach high-risk capability thresholds in Biological, Chemical, Cyber, or AI Self-Improvement categories, even with adversarial fine-tuning.[](https://openai.com/index/gpt-oss-model-card/)[](https://www.hardware-corner.net/guides/gpt-oss-20b-gpu-benchamrks/)

## Citation

```bibtex
@article{cheng2023optimize,
  title={Optimize weight rounding via signed gradient descent for the quantization of llms},
  author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi},
  journal={arXiv preprint arXiv:2309.05516},
  year={2023}
}
```