Local-LLM-User-Guideline
This guide delves into the features and differences of Local Large Language Models (LLMs), emphasizing privacy management, versatility, and open-source community contributions. It compares online and local setups, considering privacy safeguarding, cost efficiency, and management aspects of solutions like GPT, LLama, and Mistral. The publication discusses viable scenarios for on-premises LLM application, such as environments with sensitive data, task diversity, and high-volume data handling. Community-driven development is promoted, while recognizing the difficulties of self-managing these systems. It's a crucial resource for those aiming to understand AI's changing landscape with an emphasis on independence and data protection.