What are the limitations of GPUs in AI?

GPUs face several limitations when used for artificial intelligence tasks. One primary constraint is their limited high-bandwidth memory relative to their computational capabilities. Carl Shulman explains that leading Nvidia chips can perform hundreds of teraflops but only have 80GB to 160GB of high-bandwidth memory. This imbalance means that managing AI models with extremely large weights (e.g., 80 terabytes) would require a significant number of GPUs. While data parallelism would allow such models to operate across multiple tasks, the fundamental restriction of memory relative to computational output remains a bottleneck 1.

Additionally, the advancement of AI software and the rapid growth of AI budgets exacerbate the need for more effective GPUs. As AI software improves, existing GPUs can handle more complex tasks, leading them to require even greater compute capacity, which pushes the existing hardware limitations further 2.