Alex Zhang and others thought that enough data could enable GPU-kernel development to be scaled and automated, analogously to Codeforces' large problem collection.

“yeah yeah yeah and there's like there's actually a surprisingly small space of optimizations that people do and there's actually not that many kernels per se that people are interested in optimizing and so we kind of had this thought that like if you had enough data like in the same way that code forces”

“millions of problems if you could do this with gpu code like you could scale and automate kind of gpu kernel development which for researchers is a huge deal because i think one of the bigger bottlenecks like if you look like mamba for example like they released the paper with kernels because otherwise like you can't really use it in any meaningful way you know and not everyone has like a tree dow on their team so we're very interested in this uh kernel bench kind of spawned from that too”