Scaling Laws for Efficient Transformer Architectures in Distributed Environments

This research explores the relationship between model architecture, compute budget, and training efficiency in distributed settings. We propose a novel set of scaling laws that allow for predictable performance gains while minimizing communication overhead across heterogeneous hardware clusters. Our findings demonstrate that strategic parameter allocation can lead to a 30% reduction in training time without compromising model accuracy.