distributed-llm-pretraining-torchtitan

Pretrain large language models using distributed parallelism in PyTorch by leveraging FSDP2, Tensor Parallelism, Pipeline Parallelism, and Context Parallelism. This approach scales efficiently across up to 512 GPUs without requiring third-party dependencies.

PyTorch distributed training supports Float8 for LLM pretraining to enable 30-50% speedups on H100 GPUs. This feature is integrated directly through torchao and requires no additional third-party libraries.

The best way to pretrain a Llama 3.1 8B model at scale is using torchtitan with a provided configuration file. You can execute the run_train script and specify the llama3_8b.toml config to start distributed training.

You can train models up to 405B parameters using torchtitan. It supports pretraining large language models from 8B to 405B and provides significant speedups over baselines using PyTorch-native distributed parallelism.

You need specific PyTorch versions to run distributed training with torchtitan. Your environment must include PyTorch version 2.6.0 or higher, torchtitan 0.2.0 or higher, and torchao 0.5.0 or higher.

Use PyTorch-native distributed parallelism over third-party frameworks to eliminate external dependencies while pretraining large language models. It provides high-performance FSDP2, TP, PP, and CP support directly within your existing PyTorch research and production environments.