2.2 Optimization
The key optimization challenge is to maintain high GPU utilization even as we scale the number of model parameters and flops. When we scale models to teraflops and beyond, we start to hit major bottlenecks in our software stack that super-linearly increase training time and reduce accelerator utilization. We require hundreds or thousands of GPUs to run just a single experiment. Improvements in accelerator utilization can lead to significant reductions in cost and improve fleet utilization. It enables us to fund more projects and run more experiments in parallel.
2.3 Numerical Stability