> For the complete documentation index, see [llms.txt](https://docs.cortensor.network/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.cortensor.network/technical-architecture/ai-inference/open-source-models.md).

# Open Source Models

Cortensor leverages open-source models to provide robust and flexible AI inference capabilities. By utilizing these models, Cortensor ensures that the network remains accessible, transparent, and adaptable to various use cases.

## **Supported Models**

### **Llama 3**:

* Available in both quantized and regular versions.
* Supports a wide range of hardware, from low-end devices to high-end GPUs.
* Enables broad participation in AI inference tasks by accommodating diverse computational resources.
* Quantization allows lower-end devices to perform inference tasks, promoting inclusivity and scalability.

**Future Plans**

* **Expansion of Llama 3 Models**: Cortensor plans to add more variations of Llama 3-based models to enhance the network’s capabilities and provide greater flexibility for different tasks.
* **Integration of Additional Open Source Models**: Beyond Llama 3, Cortensor is committed to integrating other open-source AI models. This will further diversify the network’s capabilities and ensure it remains at the forefront of AI technology.

**Benefits of Open Source Models**

* **Transparency**: Open-source models allow for greater transparency and trust within the network, as their development and updates are publicly available.
* **Community-Driven Innovation**: Leveraging open-source models encourages community contributions and collaboration, driving continuous improvement and innovation.
* **Cost-Effectiveness**: Open-source models reduce the cost barriers for implementing advanced AI capabilities, making AI inference more accessible to a broader audience.
* **Flexibility**: The use of open-source models ensures that Cortensor can adapt to new advancements and integrate various AI technologies as they evolve.
* **Quantization**: Model quantization enables lower-end devices to participate in AI inferencing, enhancing the network’s inclusivity and resource utilization.