Distributed Training Techniques: DDP, Pipeline Parallelism, and FSDP
Modern deep learning models have grown exponentially in size and complexity. GPT-4 has over a trillion parameters, and even “smaller” models like LLaMA-70B require substantial computational resources. Training or fine-tuning such models on a single GPU is often impossible; not just because of time constraints, but because the model itself may not fit in the memory of a single device. This is where distributed training becomes essential.
Why Do We Need Distributed Training? The Memory Wall Problem A modern GPU like the NVIDIA A100 has 80GB of memory. Sounds like a lot? Let’s do some math:
