Recent trends in language modeling have focused on increasing performance
through scaling, and have resulted in an environment where training language
models is out of reach for most researchers and practitioners. While most in
the community are asking how to push the limits of extreme computation, we ask
the opposite question: How far can we get with a single GPU in just one day?
We investigate the downstream performance achievable with a transformer-based
language model trained completely from scratch with masked language modeling
for a single day on a single consumer GPU. Aside from re-analyzing nearly all
components of the pretraining pipeline for this scenario and providing a
modified pipeline with performance close to BERT, we investigate why scaling
down is hard, and which modifications actually improve performance in this
scenario. We provide evidence that even in this constrained setting,
performance closely follows scaling laws observed in large-compute settings.
Through the lens of scaling laws, we categorize a range of recent improvements
to training and architecture and discuss their merit and practical
applicability (or lack thereof) for the limited compute setting.Comment: 22 pages, we provide code at https://github.com/JonasGeiping/crammin