Our World in Data GPU performance

This source offers a critical analysis of GPU computational performance, examining how many floating-point operations per second can be achieved per dollar of hardware investment. By tracking GPUs specifically used for training large AI models (over 1 billion parameters), the research provides insights into the evolving landscape of AI computational infrastructure. The methodology is particularly noteworthy for its nuanced approach, acknowledging that raw hardware metrics only tell part of the story. The analysis recognizes that software and algorithmic advances can deliver substantial performance improvements independent of hardware upgrades. By using 32-bit precision measurements and noting that real-world performance might differ due to lower precision calculations, the source provides a balanced and forward-looking perspective on AI computational capabilities.