distributed-llm-pretraining-torchtitan

Distributed LLM pretraining with 4D parallelism is enabled by configuring FSDP2, tensor parallelism, pipeline parallelism, and context parallelism within TorchTitan to scale across 8 to 512+ GPUs. This PyTorch-native approach simplifies scale-out without external orchestration.

4D parallelism in LLM pretraining combines Fully Sharded Data Parallel 2 (FSDP2), tensor, pipeline, and context parallelism. It allows scalable model sharding across multi-GPU clusters, maximizing throughput for large architectures like Llama 3 or DeepSeek V3.

Yes, TorchTitan supports Float8 and distributed checkpointing for LLM pretraining. Float8 maximizes training throughput on multi-GPU clusters, while interoperable checkpoints ensure reliability and support end-to-end training pipelines.

Prerequisites for 4D parallel LLM pretraining include PyTorch version 2.6 or higher, torchtitan 0.2.0 or higher, and torchao 0.5.0 or higher. You also need a multi-GPU cluster and configured seed checkpointing to run the training pipelines.

Yes, you can pretrain custom architectures using TorchTitan and FSDP2. The framework applies 4D parallelism configs and PyTorch-native integration to support custom models alongside Llama 3.1 and DeepSeek V3 at scale.

PyTorch-native distributed pretraining reduces reliance on external orchestration by leveraging existing DL stacks and workflows. It integrates torch.compile and 4D parallelism directly, streamlining model sharding and scale-out across 512+ GPUs.