Description
For most of the history of computing, “doing the math right” meant picking a single, fixed floating-point format and using it everywhere. Today, that assumption no longer holds. Modern GPUs and other AI accelerators support a growing menu of numerical formats — FP64, FP32, FP16, BF16, FP8, and even FP4 — and vendors increasingly market their hardware around how fast it runs each one.
For technical decision-makers who are not hands-on developers, understanding what these formats mean and what the differences imply for technology and business decisions matters more than ever. Arithmetic precision has become another major lever for performance, alongside clock speed and parallelism, and that lever is now something software and algorithms can tune deliberately rather than something fixed by hardware alone.
