distributed-llm-pretraining-torchtitan

PyTorch LLM pretraining on 512 GPUs uses composable 4D parallelism, orchestrating FSDP2, tensor, pipeline, and context parallelism to scale models like Llama 3.1 405B across multi-GPU clusters without manual orchestration.

Scaling distributed training from 8 to 256 GPUs is handled natively by PyTorch using FSDP2 and tensor parallelism, allowing you to pretrain models like Llama 3.1 70B with fault-tolerant distributed checkpointing and SLURM multi-node launches.

PyTorch distributed training supports Float8 and torch.compile optimizations to deliver H100-scale training speedups, integrating torchao support to accelerate large language model pretraining across multi-GPU clusters.

You can export HuggingFace models for distributed pretraining using built-in HuggingFace interoperability, enabling seamless conversion and export for PyTorch-native FSDP2 training workflows across multi-node clusters.

You do not strictly need SLURM to launch multi-node distributed training for large language models, as PyTorch-native workflows provide both single-node and SLURM multi-node recipes for flexible cluster orchestration.

Async checkpointing during multi-GPU LLM pretraining prevents training stalls by writing distributed checkpoints without blocking the compute pipeline, ensuring fault-tolerant recovery for 4D parallelism jobs across 512+ GPUs.