Build and Run GPU Infrastructure for AI

For ML engineers, MLOps and platform engineers, infrastructure architects and DevOps people who know Linux, Python and basic containers. Plan, build, run and pay for GPU infrastructure for AI training and serving as it stands in November 2026: how GPUs work and what limits them, CUDA and its libraries, training on one GPU and many with DDP, FSDP2 and checkpoints, interconnects, storage and cluster sizing, GPUs in containers and on Kubernetes with Dynamic Resource Allocation, sharing and queues, serving engines such as vLLM and Dynamo, cloud capacity, cost, monitoring and security. Follow Galebrook Analytics as it sizes and runs its own models, then load-test a model server and size a fleet yourself in a free lab.