AI training is spreading across datacenters, putting new demands on networks

Training a large AI model takes thousands of processors working together.

They regularly share numerical results and must wait until the exchange is complete before continuing.

When training spans datacenters, network delays can leave processors waiting and make training take longer.

Spreading the work can give companies access to more space and power, but it makes fast, reliable connections more important.