Efficient model parallelism enables training transformers at scales previously impractical — the engineering foundation for training all subsequent large language models.
Presents efficient model parallelism techniques for training transformer models up to 8.3B parameters. Introduces intra-layer parallelism that splits attention heads and MLP layers across GPUs with minimal communication overhead.