Exponential Growth of Large Language Models
In recent years, the growth of large language models (LLMs) has been exponential, driven by factors like access to vast data, powerful hardware, and improved training algorithms. Models have doubled in size every few months, with GPT-4 reaching 1.8 trillion parameters in 2023. Larger models offer superior context understanding and accuracy in complex tasks like dialogue systems and question-answering. Researchers predict models could surpass 100 trillion parameters, approaching the complexity of the human brain. Techniques like sparse training promise efficiency while maintaining high model quality.
LLM Training Cost
The rapid growth of large language models (LLMs) has led to soaring training costs, primarily driven by exponential increases in model size, growing at 180% annually, and the quadratic relationship between model size and computational requirements. In recent years, training costs have skyrocketed—from $1M for GPT-3 (175B parameters) in 2021 to $6.7M for GPT-4 (1.8T parameters) in 2023. By 2028, GPU costs for training could reach $19B. Despite GPU performance improvements, computational demands are outpacing these advancements, creating a “winner-takes-all” dynamic in AI innovation. This could lead to a limited number of players dominating the market, as smaller organizations struggle to keep up with the increasing costs of training state-of-the-art models.
The escalating costs of training large AI models, driven by exponential growth in model size and computational needs, have created significant barriers to entry for smaller players. As the financial burden increases, AI development is becoming more exclusive, potentially stifling innovation and concentrating power among a few large organizations.