多GPU中的AI：ZeRO与FSDP

The Zero Redundancy Optimizer (ZeRO) is designed to enhance the efficiency of multi-GPU training by reducing redundancy. Its core concept involves distributing model parameters, gradients, and optimization states across multiple GPUs, thereby lowering memory usage and improving computational efficiency. Implementing ZeRO involves several steps, including parameter partitioning, gradient aggregation, and state updates. Users can easily implement ZeRO using the PyTorch framework, and by combining it with Fully Sharded Data Parallel (FSDP) technology, further optimization of large-scale model training is achievable. FSDP enhances memory utilization and training speed by distributing different parts of the model across various GPUs. The integration of these technologies enables the training of large deep learning models even with limited hardware resources.