Scratch to Scale: Real-World HPC Training

Large-scale training has changed fast. Fine-tuning a model on one GPU does not prepare you for what happens when a training job has to run across many GPUs and machines.

At that scale, you have to understand how the whole system behaves. Communication between GPUs can become expensive. A poor choice of parallelism can waste memory or leave hardware idle. A job can look like it is running normally while a network bottleneck, an overloaded expert, or a slow worker cuts performance.

It is hard to learn this from documentation alone. Access to multi-node GPU clusters is limited, and many examples are small enough that the problems you see at scale never appear.

This course gives you access to a real cluster where you can work through those problems yourself. You will write distributed training code, inspect what happens across the cluster, measure where time and memory are going, and run workloads large enough for these decisions to matter. The course is built around the kind of work engineers face when training and running large models across many GPUs.