training-llms-megatron

Train large language models using Megatron-Core by distributing workloads across GPUs with tensor, pipeline, sequence, context, and expert parallelism. This approach supports models from 2B to 462B parameters on multi-node NVIDIA GPU clusters.

Training a 462B parameter model requires combining multiple parallelism strategies including tensor, pipeline, sequence, context, and expert parallelism to achieve high GPU throughput and distribute the workload efficiently across hardware.

Megatron-Core supports expert parallelism for MoE training alongside tensor, pipeline, sequence, and context parallelism. This enables throughput-optimized workflows for large-scale models on multi-node GPU clusters.

You can use Megatron-Core for pretraining LLaMA variants on H100 clusters. It provides production-grade configurations to enable distributed training across NVIDIA GPUs using multiple parallelism strategies.

Scale LLM training from 2B to 462B parameters by leveraging Megatron-Core's advanced parallelism across GPUs. This approach distributes workloads efficiently using production-grade setups on multi-node GPU clusters.