How to Train Billion-Parameter Models: DeepSpeed ZeRO vs. PyTorch FSDP
About this lesson
Ever wonder how companies train models with billions of parameters without running out of GPU memory? In this video, we explore two of the most powerful tools for distributed training: DeepSpeed and Fully Sharded Data Parallel (FSDP). We break down how these frameworks bridge the memory gap and help you train larger, faster models. What you’ll learn in this technical guide: The Memory Problem: A breakdown of why model weights, gradients, optimizer states, and activations quickly exceed the capacity of even the best GPUs. DeepSpeed ZeRO Explained: A step-by-step guide to ZeRO-1, ZeRO-2, and ZeRO-3, and how they shard memory across your cluster to enable massive scale FSDP vs. DeepSpeed: A head-to-head comparison to help you choose the right tool—whether you prefer DeepSpeed's mature optimizations or PyTorch's native FSDP integration. Optimization Power-Ups: How to combine techniques like Activation Checkpointing and Mixed Precision (BF16) to maximize your throughput and minimize memory footprint. Practical Implementation: Real-world examples on when to use each stage and how to start optimizing your training pipeline today. Whether you are fine-tuning an LLM or pre-training from scratch, these are the techniques that power modern AI. #DeepLearning #AI #DistributedTraining #DeepSpeed #PyTorch #FSDP #LLM #MachineLearning #AIEngineering #DataScience #ArtificialIntelligence #AIAcademy
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