Schedulers place jobs across nodes, while high-speed networks synchronize gradients, parameters, and checkpoints. Storage, fault recovery, and balanced device utilization are as important as raw compute.
A training cluster is a group of connected computers and accelerators used to train models at distributed scale.
Schedulers place jobs across nodes, while high-speed networks synchronize gradients, parameters, and checkpoints. Storage, fault recovery, and balanced device utilization are as important as raw compute.