distributed-llm-pretraining-torchtitan

Distributed LLM pretraining across multiple GPUs uses PyTorch-native parallelism techniques like FSDP2, tensor parallelism, pipeline parallelism, and context parallelism. This enables efficient scaling and memory optimization for large language models on modern multi-node GPU clusters.

4D parallelism combines FSDP2, tensor parallelism, pipeline parallelism, and context parallelism for Llama pretraining. You need it when scaling models across large GPU clusters where memory constraints and execution efficiency require distributing both computation and memory across multiple dimensions.

Float8 acceleration and torch.compile optimization are fully supported for distributed training. These performance optimization techniques work alongside activation checkpointing to maximize GPU utilization and accelerate large-scale model pretraining workflows.

Custom model architectures like DeepSeek and Llama are supported through custom model integration workflows. The system accommodates custom architectures within distributed training scenarios, providing the necessary flexibility for non-standard model designs.

Distributed checkpoint management handles fault-tolerant saves while providing Hugging Face conversion workflows. This approach ensures checkpoint interoperability, allowing pretrained models to be seamlessly converted and loaded into downstream Hugging Face environments.

PyTorch-native parallelism provides a production-oriented workflow without external framework dependencies. It integrates FSDP2, torch.compile, and Float8 natively, reducing compatibility issues while offering fine-grained control over 4D parallelism configurations for custom architectures.