distributed-llm-pretraining-torchtitan

Distributed LLM pretraining uses composable 4D parallelism, combining FSDP2, tensor, pipeline, and context parallelism to scale multi-GPU infrastructure into usable throughput for large models from 8 to 512+ GPUs.

Yes, Float8 training is supported via torchao alongside torch.compile integration to achieve throughput gains on H100 GPUs during large-scale transformer pretraining.

Scaling Llama 3.1 pretraining across a multi-node cluster is handled using PyTorch-native configurations that support SLURM multi-node execution and resumable distributed checkpointing for fault tolerance.

Yes, resumable distributed checkpointing is supported through DCP checkpoint interoperability, ensuring fault-tolerant training sessions can be paused and recovered during large-scale scratch pretraining.

FSDP2 is used for sharded training to enable efficient memory distribution, acting as a core component of the 4D parallelism strategy required to scale custom transformer variants across multiple nodes.

Context parallelism and pipeline parallelism are combined to split model layers and sequence lengths across devices, preventing memory bottlenecks when scaling custom transformer architectures to 512+ GPUs.