Local inference must fit model weights, caches, activations and software overhead into accessible memory. The required amount changes with model size, quantization, context length and runtime implementation.
A system with insufficient capacity may fail to load the model or use slower storage and memory paths. Users should compare measured peak usage with available memory rather than relying only on the model file size.
ELI5
Hardware memory capacity is the amount of fast memory available while an AI model is running. The memory must hold the model plus temporary working information and the active conversation or other context.
For example, a model file may fit on a graphics processor, but a long document can still exceed the remaining memory needed for processing. More capacity lets the system handle larger models or more simultaneous work, though it does not automatically make the model faster.
Why does an AI model need more memory than its file size?
The runtime also needs caches, activations, buffers and software overhead while the model is running.
What settings change local AI memory demand?
Model size, precision, context length, batch size and runtime implementation can all change it.
