Gaming the system was fine for a while, now it's time to get precise about precision
A multi-exaflop supercomputer the size of your mini-fridge? Sure, but read the fine print and you may discover those performance figures have been a bit … stretched.
As more chipmakers bake support for 8-bit floating point math into next-gen silicon, we can expect an era of increasingly wild AI performance claims that differ dramatically from the standard way of measuring large system performance, using double-precision 64-bit floating point or FP64. When vendors shout about exascale performance, be aware that some will use FP8 and some FP64, and it's important to know which is being used as a metric. A computer system that can achieve 200 peta-FLOPS of FP64 is a much more powerful beast than a system capable of 200 peta-FLOPS at just FP8.
These performance numbers are not technically fabrications, but there is certainly a new kind of numbers game afoot – especially among the supercomputing set. It appears some are taking the opportunity of the rise in machine-learning workloads to break away from using FP64, and use the lower FP8, to make their performance numbers seem immense. There is some logic to it, as AI models don't need the full FP64 and in fact will do pretty much just as well with a lower precision, such as FP8.
AI startup Graphcore contends that standardizing on FP8 as an industry will allow for better machine-learning performance and efficiency while enabling"seamless interoperability" of workloads across systems for training and inference. More likely, the AI startup sees FP8 as an opportunity to level the playing field and win customers away from Nvidia, AMD, and others.
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