MLOps & Infrastructure · advanced · concept 145 of 176

⚙️Distributed Training

Training models across multiple GPUs or machines. Essential for frontier models, which are trained on clusters ranging from tens of thousands to several hundred thousand accelerators. Techniques: data parallelism, model parallelism, pipeline parallelism. Gradient accumulation fakes bigger batches on small GPUs, and checkpointing is what lets thousand-GPU runs survive hardware failures.