training-llms-megatron

3D parallelism combines tensor, pipeline, and data parallelism to train models efficiently across multiple GPUs. Megatron-Core implements these strategies on NVIDIA H100 hardware, enabling you to scale training from 2B to 462B parameter models by distributing computation across dimensions rather than sequentially.

Megatron-Core supports tensor parallelism, pipeline parallelism, data parallelism, sequence parallelism, context parallelism, and expert parallelism. These 6 techniques can be combined and configured independently to optimize throughput and memory usage for models ranging from 2B to 462B parameters on production clusters.

Yes, Megatron-Core enables expert parallelism for mixture-of-experts deployments. It handles MoE configurations alongside 3D parallelism, precision options including BF16 and FP8, and optimization features like Flash Attention to deliver production-ready training pipelines for sparse expert models.

Megatron-Core supports BF16 and FP8 precision options paired with Flash Attention and transformer_engine optimizations. These reduce memory overhead and accelerate computation during distributed training, enabling efficient use of H100 resources for ultra-large model training.

Yes, Megatron-Core includes pretrain_gpt.py scripts, data handling paths, vocabulary and merge utilities, checkpointing for fault tolerance, and distributed optimization strategies. These tools enable scalable, production-ready training deployments with built-in reliability patterns.

Megatron-Core is optimized for NVIDIA H100 hardware in production-grade clusters. It scales across distributed nodes using configurable tensor, pipeline, and data parallelism to handle models from 2B to 462B parameters, requiring multi-GPU coordination and network bandwidth for cross-node communication.