Large Scale Distributed Training and Parallel Computing Questions

Understand strategies for training models at scale: data parallelism, model parallelism, pipeline parallelism, and hybrid approaches. Address synchronization, gradient compression, all-reduce operations, and communication efficiency. Discuss handling hardware failures, reproducibility, and memory/compute trade-offs. For Staff-level, discuss training 100B+ parameter models.