distributed-llm-pretraining-torchtitan

To pretrain LLMs like Llama 3.1 on multiple GPUs, you can use this PyTorch-native framework to run distributed training across 8 to 512+ GPUs with 4D parallelism. It provides the necessary scripts to initiate the process efficiently.

4D parallelism in distributed LLM training composes FSDP2, Tensor Parallelism, Pipeline Parallelism, and Context Parallelism to maximize throughput. You need it when pretraining massive models that exceed the memory of a single GPU cluster.

Yes, PyTorch distributed training supports Float8 precision and torch.compile for large models. This framework integrates these features alongside distributed checkpointing to enhance speed and overall training efficiency.

Yes, you can use TorchTitan for pretraining DeepSeek V3 models on a 256 GPU cluster. It is specifically optimized to scale training for massive models by combining FSDP, TP, and PP to achieve optimal throughput.

The best way to start pretraining a Llama 3.1 8B model on 8 GPUs is to run the provided training script with the specific Llama 3.1 8B configuration file, which sets up the distributed environment natively in PyTorch.