distributed-llm-pretraining-torchtitan

To configure distributed LLM pretraining, you coordinate 4D parallelism: FSDP2, tensor parallelism, pipeline parallelism, and context parallelism. This setup allows you to scale PyTorch training workloads efficiently across 8 to 512+ GPUs in single-node or multi-node clusters.

The best way to improve pretraining throughput on H100 hardware is to enable torch.compile and Float8 training. These performance optimizations significantly increase processing speed during large-scale LLM pretraining jobs on newer hardware.

Yes, you can resume LLM pretraining from a checkpoint and convert it for HuggingFace. The workflow uses PyTorch Distributed Checkpoint to save, resume, convert, and shard checkpoints, ensuring full HuggingFace interoperability for your models.

Yes, this distributed training setup supports custom transformer models. You can add new model families by defining model arguments, parallelization logic, and optional state-dict adapters alongside pre-configured models like Llama 3.1 and DeepSeek V3.

You set up 4D parallelism for Llama 3.1 pretraining by requesting a launch-ready training plan. This configuration coordinates FSDP2, tensor, pipeline, and context parallelism tailored to your specific GPU count and checkpointing requirements.