When people talk about AI infrastructure, they talk about GPUs. The chip announcements, the cluster sizes, the compute benchmarks. This is understandable — compute is expensive, visible, and easy to measure. It is also frequently not the bottleneck.
A GPU is only useful when it has data to process. During training, that means a continuous stream of batches from the training dataset. During inference, it means prompt tokens arriving fast enough to keep the model busy. When the data pipeline can't keep up, the GPU sits idle. You are paying for the most expensive part of the system while it waits for the cheapest part to catch up. This is the storage problem in one sentence.
AI Storage covers the systems designed to solve this. Training datasets for large models can run to petabytes. Reading that data fast enough to feed a GPU cluster requires storage architectures that are very different from what a typical web application needs. The access patterns are different, the throughput requirements are different, and the cost tradeoffs are different.
The networking problem is similar but shows up in distributed training. When a model is too large to train on a single machine, the work is split across many GPUs connected by a network. After each training step, those GPUs need to synchronize their parameter updates. The time this takes is determined almost entirely by the network bandwidth between them. A slow interconnect turns a theoretically fast cluster into a slow one, because the GPUs spend most of their time waiting for each other rather than training.
AI Networking covers the high-speed interconnects and network architectures that make distributed training practical. The difference between commodity networking and purpose-built AI networking can be the difference between a training run that takes days and one that takes weeks.
The articles in this section cover both layers. They explain why the infrastructure decisions that don't make headlines are often the ones that determine whether a system actually performs.
