training-llms-megatron

Train large language models over 1B parameters efficiently using NVIDIA Megatron-Core on H100 GPUs. It applies advanced parallelism strategies including tensor, pipeline, sequence, and expert parallelism to maximize GPU utilization across distributed nodes.

Megatron-Core supports tensor, pipeline, sequence, and expert parallelism for distributed training. These advanced parallelism strategies optimize GPU utilization and enable scalable training across multiple GPUs and nodes for large language models up to 462B parameters.

Yes, you can use Megatron-Core to train models on H100 GPUs. The framework is specifically optimized for maximum GPU efficiency on H100 hardware, enabling high-efficiency training for large language models ranging from 2B to 462B parameters.

Yes, training models with Megatron-Core requires apex and transformer-engine dependencies. The framework also requires torch and megatron-core to execute large language model training scripts with advanced parallelism strategies on H100 GPUs.

Start large language model training using Megatron-Core by running the Docker container `nvcr.io/nvidia/pytorch:25.04-py3` with `--gpus all` or installing the framework via `pip install megatron-core` to execute training scripts.

The best way to scale distributed training across multiple nodes is using Megatron-Core with advanced parallelism strategies. It enables scalable training for large language models up to 462B parameters by distributing workloads across multiple GPUs and nodes efficiently.