A classifier has a right answer to check against. A generative model's output has no correct answer at all — "is this a good generated cat image" has no ground truth to compare against, only a distribution to compare against. Every metric in this page is a different proxy for that comparison, and every proxy can be gamed.
The hardest part of working with language models isn't building one — it's knowing, with any confidence, whether the new version is actually better. There's no single number that captures "good," and the real skill in evaluation is knowing each imperfect proxy's specific blind spot well enough to know when it's lying to you.
Accuracy is the wrong metric more often than it is the right one. A model that predicts "healthy" for every patient in a dataset where 99% of patients are healthy scores 99% accuracy while being completely useless. Picking the right metric means picking it from the cost of each error type, not from convention.
Regression metrics all try to say "how close were the predictions" in a single number, but they disagree about what "close" means — and that disagreement matters exactly when outliers are present or when comparing models. Knowing which metric lies to you in which situation is the actual skill.
Nsight Compute organizes hundreds of hardware counters into sections; Speed of Light tells you whether a kernel is memory-bound, compute-bound, or latency-bound, but not which specific resource inside that category is the bottleneck. This page is the metric-by-metric reference for answering that second question — the counters worth reading once Speed of Light has pointed at a direction, what a good value looks like, and what to change when it isn't.