Running Large Language Models Privately - privateGPT and Beyond

AI Impact Summary

The rise of large language models necessitates a shift in how organizations approach data privacy and security. This analysis focuses on the emerging trend of privately running LLMs, highlighting techniques like federated learning, homomorphic encryption, and locally deployed models. Organizations are increasingly concerned about data exposure and breaches when relying on centralized cloud-based LLM services, leading to a push for solutions that maintain control and protect sensitive information, exemplified by the exploration of privateGPT and h2oGPT leveraging open-source models like LLaMA.