distributed-llm-pretraining-torchtitan

Pretrain Llama models from scratch using PyTorch by orchestrating 4D parallelism techniques like FSDP2, Tensor Parallelism, and Pipeline Parallelism to maximize hardware utilization on multi-node GPU clusters.

4D parallelism for distributed LLM training combines FSDP2, Tensor Parallelism, Pipeline Parallelism, and Context Parallelism to efficiently scale model training across multiple GPUs for models ranging from 8B to 405B parameters.

Float8 quantization speeds up training on H100 clusters by integrating with torch.compile to achieve significant performance optimizations during large-scale language model pretraining.

Configure distributed training for a Llama 3.1 8B model on 8 GPUs by using predefined registry-based configurations and launch scripts to execute the pretraining job efficiently on a single node.

This distributed training framework requires PyTorch version 2.6.0 or higher, along with torchtitan 0.2.0 and torchao 0.5.0, to support native 4D parallelism and Float8 training workflows.

Manage checkpoints during large-scale pretraining by utilizing the framework's optimized checkpointing features, which support interoperable checkpoint management for production-grade model development.