AI Computing Power · KAEL Editorial Department
Computing power planning: define the problem first, then select resources
The starting point for computing power construction is to clarify research tasks and delivery requirements. The same device configuration may produce completely different user experiences under different data scales, model structures, and response requirements.
Split task load
Record data preprocessing, model training, parameter search, and inference separately. Preprocessing may be limited by storage speed, training may require more video memory, and inference is more concerned with response time. If you mix these links into one total time-consuming, it is easy to buy the wrong resources.
Establish a small-scale baseline
First complete an experiment with representative data, record the running time, memory peak and device utilization, and then expand the input size. Observing growth trends is more valuable than copying other teams' configurations.
Let resources serve research
Consider queuing time, failed reruns, and result storage when planning. Equipment performance is only one part of the equation; a set of stable and repeatable experimental procedures can truly shorten the research cycle with new computing power.
