CIFAR-10 speedrun: 94% with the fewest FLOPs

Train an image classifier to 94% mean test accuracy on CIFAR-10 using as little training compute as possible. A submission is a PyTorch training script; the lab counts every FLOP it runs and averages accuracy over several seeds on other contributors' machines.

Training small models is mostly trial and error, and this is the public benchmark for doing it efficiently. Tricks that cut compute here have carried over to large models: the Muon optimizer was proven in speedruns like this one before it was used to train large language models such as Moonshot AI's Kimi K2. The best known script reaches 94% with 0.29 PFLOPs.