Inside FSDP with PyTorch and Ray: Scaling Model Training with Fully Sharded Data Parallel
This blog post provides a detailed explanation of Fully Sharded Data Parallelism (FSDP) in PyTorch, a technique for efficiently training large AI models across multiple GPUs. It covers the internal workings of FSDP, demonstrating how it shards model parameters, gradients, and optimizer states to minimize memory usage per GPU. The post includes practical examples, such as training a Vision Transformer and fine-tuning a Qwen3-TTS voice cloning model using PyTorch and Ray Train. AI
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