Large language model with elastic resources

Abstract

Aspects of the invention include techniques for providing a large language model with elastic resources. A non-limiting example method includes training a large language model within a parallelized training environment that includes a set of training resources. The large language model is split into a plurality of pieces and each training resource trains over one piece. Baseline metrics are collected for the parallelized training environment. The method includes determining whether a current number of available training resources is an integer multiple of a minimum threshold of training resources and initializing one or more try-runs to evaluate vertical scaling and/or horizontal scaling responsive to the determination. A try-run having a highest improvement metric is identified and, responsive to the highest improvement metric being greater than a predetermined threshold, a training pattern is updated using one or both of a vertical scaling and a horizontal scaling of the set of training resources.