When people think about AI infrastructure, most attention naturally gravitates toward model trainin…
انتشار: 2026/08/10 15:29 UTCدریافت: 2026/08/13 11:32 UTCآخرین مشاهده: 2026/08/13 11:32 UTC
When people think about AI infrastructure, most attention naturally gravitates toward model training. Training large models requires massive datasets, distributed compute, and specialized hardware accelerators. The engineering involved in orchestrating training jobs across clusters of graphics processing units (GPUs) or tensor processing units (TPUs) is significant, and it's often the most visible part of the AI lifecycle.Inference, by contrast, appears deceptively simple. Once a model's been trained, the assumption is serving predictions should be straightforward: load the model, send requestvia Red Hat Blog ift.tt/YF3oy1I
