distributed-llm-pretraining-torchtitan

Pretrain LLMs across multiple GPUs using 4D parallelism by configuring distributed TorchTitan pipelines. This applies FSDP2, tensor, pipeline, and context parallelism to train models like Llama 3.1 on clusters of 8 to 512 GPUs.

Float8 training for LLM pretraining is supported in TorchTitan via the optional torchao dependency. This optimization helps accelerate large-scale model training while utilizing the PyTorch native pipeline.

The best way to configure distributed training for DeepSeek V3 on 512 GPUs is using TorchTitan with 4D parallelism. It handles complex parallelism, checkpointing, and hardware optimizations to reduce configuration errors.

You cannot use this distributed training pipeline with PyTorch 2.5 because it requires PyTorch version 2.6 or higher. You also need TorchTitan version 0.2 or higher to run the training scripts successfully.

Checkpointing and 4D parallelism work during LLM pretraining by utilizing PyTorch Distributed Checkpoint (DCP) for efficient state saving. Simultaneously, 4D parallelism splits the model and data across FSDP2, tensor, pipeline, and context dimensions.

Large-scale LLM pretraining requires specific parallelism configurations because distributing workloads across hundreds of GPUs is highly complex. TorchTitan automates these 4D parallelism and checkpointing setups to prevent time-consuming errors.