distributed-llm-pretraining-torchtitan

To pretrain a large language model from scratch, this framework orchestrates PyTorch-native 4D parallelism, combining FSDP2, Tensor Parallelism, Pipeline Parallelism, and Context Parallelism to scale training across multi-node GPU clusters.

4D parallelism is the simultaneous application of FSDP2, Tensor, Pipeline, and Context Parallelism. You need it for LLM pretraining when scaling models from 8B to 405B+ parameters across 8 to 512+ GPUs where memory and compute bottlenecks occur.

Yes, you can accelerate distributed pretraining on H100 clusters by leveraging native Float8 training and torch.compile optimizations, which provide significant speedups and memory efficiency during large-scale model training.

The best way to configure distributed training for a Llama 70B model on a SLURM cluster is using the framework's composable 4D parallelism techniques, which manage distributed parallelism and hardware acceleration across multi-node environments.

Yes, you need PyTorch version 2.6.0 or higher to run FSDP2 and 4D parallelism training, along with torchtitan 0.2.0 and torchao 0.5.0 or above for high-performance computing environments and interoperable checkpoints.

This framework provides interoperable checkpoints for scalable training workflows by utilizing PyTorch-native distributed parallelism techniques, ensuring training states are consistently saved and restored across large-scale multi-node GPU configurations.