distributed-llm-pretraining-torchtitan

Pretrain an LLM using 4D parallelism in PyTorch by leveraging FSDP2, Tensor Parallelism, Pipeline Parallelism, and Context Parallelism. This approach scales pretraining for models like Llama 3.1 8B efficiently from 8 to 512+ GPUs.

Scaling distributed training for Llama 3.1 across hundreds of GPUs is best achieved using 4D parallelism. This method combines FSDP2, Tensor, Pipeline, and Context Parallelism to optimize resource utilization for large-scale pretraining.

TorchTitan supports Float8 precision for pretraining on NVIDIA H100 GPUs. This optimization provides significant speedups during large-scale distributed LLM pretraining while maintaining a composable, PyTorch-native ecosystem.

You can use PyTorch-native tools to pretrain DeepSeek V3 models. This Skill enables distributed pretraining from scratch using TorchTitan, supporting architectures up to 400 billion parameters with distributed checkpointing.

For distributed LLM pretraining with FSDP2 and torch.compile, you need a multi-GPU environment. This Skill provides scripts and configuration files to launch training across 8 to 512+ GPUs without external dependencies.