Vietnam's Strategic Dilemma in Large Language Model Development: Comparative Lessons and Policy Imperatives

Vietnam's aspiration to become a regional AI hub by 2030 faces significant challenges in large language model (LLM) development. While policy frameworks such as the National Strategy on AI Development to 2030 demonstrate political commitment, implementation gaps persist. This paper examines Vietnam's LLM development challenges through comparative analysis with China's dual approach of open and closed models, Southeast Asia's emerging strategies, and alternative pathways from India and Arab nations. We identify critical barriers in data infrastructure, talent development, computational resources, and regulatory frameworks. Drawing on international experiences, we propose a strategic roadmap emphasizing open-source collaboration, targeted investment, and regional cooperation. Our analysis reveals that Vietnam must leverage its linguistic and cultural uniqueness while adopting pragmatic open-source strategies to avoid technological dependency and achieve AI sovereignty.