Large Language Models and Free-Software Principles: Pathways to Brazilian Digital Sovereignty - Melo, Tiago de (Anais do Congresso Latino-Americano de Software Livre e Tecnologias Abertas (Latinoware); 2025: Anais do XXII Congresso Latino-Americano de Software Livre e Tecnologias AbertasDO - 10.5753/latinoware.2025.14977, 2025)
This paper critically examines how contemporary Large Language Models, LLMs, align with free software principles and what that alignment, or lack of alignment, means for Brazil and its pursuit of digital sovereignty. We introduce a three tier openness taxonomy that distinguishes proprietary, semi open, and fully open models, and we audit eighteen leading systems for license terms, data transparency, and reproducibility. Energy projections indicate that pre training a model with one hundred billion parameters demands about six million GPU hours and around one gigawatt hour of electricity, far beyond the ten PFLOPS currently available in Brazil’s public high performance computing facilities. Our analysis identifies four systemic bottlenecks: outdated infrastructure, limited Portuguese corpora, prohibitive economic costs, and persistent talent flight. Verified national initiatives such as SoberanIA, CPQD’s GPT BR 2.8B, SerproLLM, and the open data communities Querido Diário and Brasil.io show that combining global checkpoints with local fine tuning can reduce training costs by as much as ninety percent. Based on these findings, we recommend, first, multiyear investment in distributed state owned GPU clusters; second, a Public Interest Data Act that prioritizes Portuguese text under Creative Commons licenses; third, procurement rules that recognize only licenses approved by the Open Source Initiative; fourth, expanded artificial intelligence residency programs to retain talent; and fifth, a Latin American consortium for multilingual pre training. We conclude that true sovereignty over large language models will emerge only through the synergy of transparent licensing, sovereign infrastructure, rich public corpora, and continuous human capital development.