AI Computing Power · KAEL Editorial Department
Training and inference: two different computing tasks
Training determines how the model learns, and inference determines how the model is used. Distinguishing these two stages helps to understand computing power requirements and avoids taking the performance of the experimental environment directly as actual service capabilities.
Training focuses on iteration
Training requires saving intermediate states and updating parameters repeatedly. Batch size, input length and optimization method will affect resource usage, so when comparing different solutions, you need to keep the experimental conditions consistent.
Reasoning attention service
Inference typically does not update parameters, but faces uneven request arrival times, varying input lengths, and latency constraints. A single test is fast, but it does not mean that it is still stable when multiple requests come in at the same time.
Set indicators separately
The research phase can focus on the experiment completion time, and the service phase should record delay distribution, failure ratio and resource cost. Evaluate both phases separately, and then discuss the trade-off between shared equipment or isolated deployment.
