> For the complete documentation index, see [llms.txt](https://whitepaper.nextgpu.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://whitepaper.nextgpu.ai/welcome/introduction.md).

# Introduction

The rapid adoption of artificial intelligence technologies has led to an increased reliance on cloud-based services for running advanced machine learning models. While these services provide powerful capabilities, they often require users to upload sensitive data to remote servers for processing. This dependency introduces concerns related to data privacy, regulatory compliance, system availability, and long-term operational costs.

Many organizations and individuals require AI solutions that operate within their own infrastructure, particularly when handling confidential information or working in environments with strict data protection requirements. At the same time, deploying AI systems locally can be complex, requiring specialized knowledge in system configuration, hardware compatibility, and software dependencies.

NextGPU addresses these challenges by providing a unified platform for local AI deployment and management. The system automates infrastructure setup, reduces technical barriers, and enables users to run advanced AI models on their own machines with minimal manual configuration. By combining automation, reliability, and privacy-focused design, the platform makes local AI accessible to a broader range of users while maintaining control over data and computing resources.