distributed-llm-pretraining-torchtitan

You scale LLM pretraining across a multi-GPU cluster using PyTorch by applying 4D parallelism (FSDP2, tensor, pipeline, and context parallelism) to distribute workloads efficiently from 8 to 512+ GPUs.

Yes, you can use Float8 support with torch.compile to achieve significant speedups on modern GPUs during large language model pretraining. This combination optimizes memory usage and accelerates compute-heavy operations.

Yes, distributed training with this approach supports interoperability with HuggingFace checkpoints. It utilizes distributed checkpointing and integrates with torchtune to ensure seamless resume and deployment capabilities.

The best way to pretrain Llama 3.1 or DeepSeek V3 at scale is using a PyTorch-native implementation with 4D parallelism and distributed checkpointing, which simplifies setup and scaling across multi-GPU clusters.

Yes, you need 4D parallelism (FSDP2, TP, PP, CP) to pretrain custom models on 512+ GPUs. It orchestrates scalable pretraining by distributing the workload across multi-GPU clusters efficiently.

FSDP2 works with tensor and pipeline parallelism during large-scale pretraining by sharding model parameters, activations, and gradients across devices, while context parallelism manages sequence lengths to optimize multi-GPU cluster memory.