# System Requirements ## **Supported Platforms** ### **Platform Support** Cortensor now supports **Linux, Windows, and macOS** platforms, providing broader accessibility for users across different operating systems. ### **CPU Requirements** Cortensor supports **both high-end and lower-end CPUs**, including those with and without **AVX instruction sets** (AVX, AVX2, AVX512). This ensures compatibility across a wide range of devices while maintaining efficient AI inference capabilities. ### **GPU Support** Cortensor fully supports **GPU acceleration** across **Linux, Windows, and macOS**, enabling faster and more efficient AI inference for complex and resource-intensive workloads. *** ## **Software Dependencies** ### **Python** Python is required to run Cortensor, as it handles various scripting, automation, and model execution tasks within the platform. ### **Docker** Docker is necessary for containerizing the Cortensor environment, ensuring consistent performance, easier deployment, and management of dependencies across different operating systems. *** ## **Current and Future Support** Cortensor has expanded its support to **Windows, macOS, and Linux**, alongside both **CPU- and GPU-based** processing. Future updates will continue enhancing **performance optimizations**, **additional hardware compatibility**, and **broader model support** to further improve accessibility and efficiency. *** **Disclaimer:** This page and the associated documents are currently a work in progress. The information provided may not be up to date and is subject to change at any time. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.cortensor.network/getting-started/system-requirements.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.