TeraFLOPS, short for “trillions of floating-point operations per second,” is a performance metric used to evaluate the computational power of processors, particularly in supercomputing and high-performance computing (HPC). Floating-point operations are essential for tasks involving complex mathematical calculations, such as simulations, graphics rendering, and machine learning. For instance, NVIDIA's Tesla K80 GPU, introduced in 2014, was one of the first consumer-accessible devices to exceed 8 teraFLOPS, setting a new standard for computing performance in scientific research and AI development. https://en.wikipedia.org/wiki/FLOPS

The teraFLOPS metric has become critical in the AI and deep learning domains, where large-scale data processing requires high-performance hardware. GPUs from companies like NVIDIA and AMD have achieved teraFLOPS-level performance, enabling real-time applications such as autonomous vehicles and natural language processing. For example, the NVIDIA RTX 3090, released in 2020, delivers 35.58 teraFLOPS of FP32 performance, making it a powerful tool for developers and researchers in both AI and gaming industries. https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3090/

In the supercomputing world, teraFLOPS have been succeeded by petaFLOPS and exaFLOPS, but the concept remains relevant for benchmarking smaller systems and consumer-grade devices. With advancements in cloud computing and edge computing, achieving teraFLOPS on compact, low-power devices has become a priority. Platforms like the Apple A-series and NVIDIA Jetson exemplify how teraFLOPS performance can be achieved efficiently in mobile and IoT applications. As hardware evolves, teraFLOPS will remain a foundational metric for computational power. https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson/