AI memory guides

Budget, place, measure, and diagnose

AI memory planning becomes clearer when the entire request path is visible. Identify the accelerator architecture, host RAM, supported model-loading behavior, representation, and workload settings. Keep the model revision and software versions with the test notes so a result can be reproduced.

The AI Mem Swap guide provides the planning overview. The offloading article follows data across GPU memory, host memory, and storage and asks whether the resulting inference is useful. The CUDA article investigates ownership, peaks, and retained references rather than treating every failure as a reason to clear caches.

Test realistic request lengths and repeated work, not only initialization. Include output quality when evaluating a smaller model or a lower-precision representation. A reduced memory footprint is helpful only when the application still performs its intended task.

Keep inference and training strategies distinct. A documented offloading path for one mode does not establish support for every other mode. The aim is a bounded, observable workload with an understandable resource budget—not a collection of workarounds that occasionally succeeds on one short input.