distributed-llm-pretraining-torchtitan

Distributed LLM pretraining with FSDP2 is configured using a native PyTorch framework that orchestrates composable 4D parallelism across single or multi-node GPU clusters. It maximizes hardware utilization by combining FSDP2, tensor, pipeline, and context parallelism.

4D parallelism for large language models combines FSDP2, Tensor Parallelism, Pipeline Parallelism, and Context Parallelism into a composable framework. This PyTorch-native approach maximizes GPU utilization without requiring third-party dependency bloat.

Float8 training is supported to achieve significant speedups for Llama pretraining on H100 hardware. It works alongside torch.compile and composable 4D parallelism strategies to optimize hardware performance during large-scale runs.

To launch a multi-node pretraining job for a Llama 3.1 8B model, use the skill's configuration registry with optimized FSDP2 settings across a cluster of H100 GPUs. A single-node run for llama3 8b can also be launched using the default configuration.

PyTorch supports fault-tolerant workflows during large-scale pretraining by implementing asynchronous checkpointing. This feature pairs with activation checkpointing to ensure production stability and memory efficiency across distributed clusters.

Distributed PyTorch pretraining supports diverse model architectures including Llama 3.1 and DeepSeek V3. These architectures are handled across single and multi-node GPU clusters using the composable 4D parallelism framework.