| dc.description.abstract | This research studies the viability of memory-based large language models (LLMs) as an eco-friendly alternative to transformer-based LLMs, addressing the increasing environmental concerns associated with the energetic demands of AI. The study evaluates memory-based models (IB1-IG, IGTree, TRIBL2 from the TiMBL package) against transformer-based models (GPT-2, GPT-Neo) across various performance metrics including next token prediction accuracy, latency and carbon emissions during the inference phase. Findings indicate that while transformer-based models achieve higher accuracy, they also have significantly greater and exponentially scaling computational costs and latency. In contrast, the IGTree model demonstrates respectable accuracy (9% to 18%) with near-zero latency and minimal, stable computational cost, positioning it as a strong candidate for sustainable AI. While memory-based architectures like IGTree can offer effective performance without the high environmental and operational costs of transformer-based counterparts, it is still important to focus on researching the whole LLM lifecycle and push for more transparency regarding AI energy usage disclosure. | |
