distributed-llm-pretraining-torchtitan

You can pretrain large language models across multiple GPUs using PyTorch by applying TorchTitan, which natively supports distributed training, 4D parallelism, and Float8 precision for massive scale.

4D parallelism composes FSDP2, Tensor Parallelism, Pipeline Parallelism, and Context Parallelism to distribute LLM pretraining across thousands of GPUs, enabling models from 8B to 512B+ parameters to train efficiently.

Yes, you can accelerate distributed training on H100 GPUs by integrating Float8 precision and torch.compile, which provide significant speedups during large-scale LLM pretraining.

Yes, it supports pretraining Llama 3.1 8B from scratch, allowing you to launch training on 8 GPUs using the default configuration file via the TorchTitan framework.

The best way to scale LLM pretraining for models over 100B parameters is using composable 4D parallelism and distributed checkpointing, which efficiently handles massive models across hundreds of GPUs.

Use TorchTitan instead of standard PyTorch to streamline complex large-scale pretraining, as it provides native 4D parallelism, distributed checkpointing, and Float8 optimizations not available out of the box.