FLOPS is a key measure of the performance of a processor or system, quantifying how many floating-point operations it can execute in one second. This metric is particularly crucial in fields where numerical computations are intensive, such as artificial intelligence, scientific simulations, financial modeling, and computer graphics. Floating-point calculations are vital in these areas because they handle very large or very small numbers, as well as fractions, with high precision.
In the context of training AI models, FLOPS is used as a standard to compare processing power across different systems. As AI models become more complex and require larger datasets for training, the demand for FLOPS in processing units like GPUs and TPUs has surged. This increased demand reflects the need for robust computational capabilities to handle the data-intensive and calculation-heavy tasks that characterize the development and deployment of advanced AI.
Moreover, FLOPS not only indicate a system’s speed but also serve as an indicator of the hardware’s potential to efficiently execute complex algorithms. As technology progresses, measurements in petaFLOPS (one quadrillion operations per second) or even exaFLOPS (one quintillion operations per second) are expected to become increasingly common, especially in supercomputers and data centers dedicated to AI and other applications requiring extreme computational power.
