The rapid integration of Artificial Intelligence (AI) into global economies has driven transformative advancements in sectors such as healthcare and agriculture. However, this technological revolution incurs significant environmental costs, particularly through substantial energy consumption and greenhouse gas (GHG) emissions. The carbon footprint of AI, stemming from energy-intensive processes like hardware
Contemporary researchers face unprecedented financial barriers when engaging with state-of-the-art language models, particularly through API-based services where costs are directly proportional to token consumption and computational resource utilisation. The challenge is compounded by increasing complexity of research tasks requiring extensive prompt engineering, iterative model interactions, and large-scale data processing operations.
The emergence of large language models (LLMs) and generative AI applications has ushered in a new era of artificial intelligence capabilities, fundamentally altering the landscape of computational requirements and associated costs. Generative AI systems, built upon transformer architectures and trained on vast datasets, have demonstrated remarkable scalability and adaptability across
