AI Mem Swap

Plan the complete AI memory workload

AI mem swap is best approached as resource planning rather than a single toggle. Model weights, temporary activations, request state, host-side processing, and concurrent work can all contribute to demand. Loading a model successfully does not establish that the longest intended request will fit.

Begin with the hardware architecture and supported framework path. Ordinary host swap, explicit CPU offloading, disk offloading, and a GPU allocator cache are different mechanisms. Changing one does not automatically fix a problem in another.

Use the AI VRAM memory swap overview to establish the resource boundaries before building the application's budget.

Preserve a reproducible configuration record

Record the accelerator, host RAM, operating system, driver, framework version, model identifier and revision, representation, device placement, and workload settings. Include input length, output limit, batch size, and simultaneous requests where those concepts apply.

Measure loading and execution separately. Keep the same input for comparative tests, then expand toward representative peak conditions. A model that works only for a tiny demonstration input has not been evaluated for the intended workload.

Quality belongs in the comparison. A smaller or lower-precision model may reduce memory demand, but it still needs to produce acceptable results for the task. Change one variable at a time so the reason for a result remains clear.

Diagnose the memory owner

For PyTorch CUDA workloads, distinguish memory associated with live tensors from memory reserved by the caching allocator. The two counters answer different questions. A large reserved value alone does not prove a leak.

Repeatedly clearing unused cached memory cannot remove tensor data that the application still needs or retains. Review output collections, notebook variables, callbacks, and request lifetimes. A fresh-process comparison can help isolate the difference between startup and repeated work.

The CUDA out-of-memory article provides a step-by-step investigation and a small measurement snippet. It focuses on reproducible ownership and demand rather than a collection of unexplained allocator settings.

Match the solution to the cause

For excessive live demand, test smaller batches, shorter requests, bounded concurrency, or a supported model representation. For unexpected growth, inspect retained state. For an intentionally oversized model, evaluate supported offloading and its effect on host memory and useful latency.

Inference and training are not interchangeable use cases. A strategy documented for inference does not establish an appropriate training configuration. Keep the feature's documented scope visible during experimentation.

Our offloading guide explains how to compare complete resource paths. The right configuration is one that meets the workload's quality, reliability, and performance requirements—not merely one that gets past initialization.

Technical reference: The PyTorch CUDA memory-management documentation describes allocated memory, reserved memory, and the limits of empty_cache().