Sovereign Large Language Models: Advantages, Strategy and Regulations

TL;DR

This paper examines the global push toward sovereign Large Language Models (LLMs) and analyzes how national strategies, regulatory frameworks, and international cooperation shape their development. It catalogs concrete case studies of state-funded and private-initiative models (e.g., Fugaku-LLM, ALLaM, QWEN) and assesses the economic and security implications of localized AI infrastructure. The authors synthesize lessons on data governance, funding mechanisms, and governance architectures to inform policy choices that balance innovation, data protection, and national security. The work emphasizes that coordinated investments, infrastructure-building, and adaptable regulatory regimes are essential to deploy culturally and linguistically aware LLMs that advance national autonomy while enabling global collaboration.

Abstract

This report analyzes key trends, challenges, risks, and opportunities associated with the development of Large Language Models (LLMs) globally. It examines national experiences in developing LLMs and assesses the feasibility of investment in this sector. Additionally, the report explores strategies for implementing, regulating, and financing AI projects at the state level.