training-llms-megatron

Train large language models using advanced parallelism on H100 GPUs by implementing tensor, pipeline, sequence, context, and expert parallelism via NVIDIA Megatron-Core to maximize GPU efficiency and achieve up to 47% Model FLOP Utilization.

The best way to scale LLM training up to 400 billion parameters is using a production-ready framework like Megatron-Core, which supports the exact training architectures used for Nemotron, LLaMA, and DeepSeek models.

Yes, Megatron-Core supports 3D parallelism for training LLaMA-style models by combining tensor, pipeline, and context parallelism to optimize throughput and efficiency across large GPU clusters.

Yes, you can use tensor and pipeline parallelism to maximize GPU efficiency for distributed training, alongside sequence, context, and expert parallelism strategies natively supported within the Megatron-Core framework.

To run distributed training with Megatron-Core, you need the torch, apex, and transformer-engine dependencies installed in your environment to support the underlying large language model training operations.

LLM training fails to achieve high Model FLOP Utilization on A100 GPUs without advanced parallelism, which this framework addresses by implementing tensor, pipeline, sequence, and context parallelism to optimize hardware utilization.