distributed-llm-pretraining-torchtitan

Pretrain a large language model using PyTorch 4D parallelism by orchestrating FSDP2, Tensor Parallelism, Pipeline Parallelism, and Context Parallelism. This framework enables training models ranging from 8B to 405B+ parameters across distributed clusters.

Yes, you can use Float8 quantization for distributed LLM pretraining on H100 clusters. The framework leverages Float8 training alongside torch.compile to achieve significant speedups and efficient GPU utilization.

The best way to scale LLM pretraining from a single node to 512 GPUs is using a unified, PyTorch-native framework. It supports SLURM-managed clusters and composable 4D parallelism to train massive 405B parameter models.

Start pretraining a Llama 3.1 8B model by executing the provided run_train.sh script with the llama3_8b.toml configuration file. This initiates the distributed training process natively within PyTorch.

Yes, distributed LLM pretraining with FSDP2 supports interoperable model checkpoints. The framework includes robust distributed checkpointing capabilities to satisfy high-performance computing environment requirements.