> For the complete documentation index, see [llms.txt](https://docs.yieldgpu.tech/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.yieldgpu.tech/welcome/executive-summary.md). # Executive Summary YieldGPU Hub is a platform that enables users to run artificial intelligence models directly on their local hardware. The system provides a secure and automated environment for deploying and managing AI workloads without requiring data to be transmitted to any external party. Therefore, ensuring greater privacy, data control, and operational independence compared to cloud-based AI solutions. The platform simplifies the process of setting up AI infrastructure by automating installation, configuration, and model deployment. Users can access AI capabilities through a user-friendly Desktop application - YieldGPU Agent, which manages system resources, maintains persistent conversation context, and supports a wide range of AI-driven tasks such as text generation, document processing, image creation, programming and creative works. YieldGPU Hub is a part of the Agent application designed for developers, researchers, organizations, and privacy-conscious users who require reliable AI functionality while maintaining full control over their data and computing environment. Hub distinguishes itself by automating the full local AI deployment lifecycle, enabling non-expert users to run advanced AI workloads without manual infrastructure configuration.
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Learn how YieldGPU works, its core concepts, and how local and distributed AI come together.image (2).avif
Learn the key objectives driving YieldGPU, including privacy, scalability, cost efficiency, and decentralized compute.image (3).avif
Learn the challenges with current AI systems and why YieldGPU was designed to solve them.image (4).avif
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