How much GPU memory do you need for local AI?

Record the workload

Write down the model or pipeline, precision, context or resolution, batch/concurrency and other GPU applications. A recommendation without those fields is not decision-grade.

Separate fits from runs well

A workload loading once is not a reliable daily workflow. Reserve headroom for the interface, additional models, larger inputs and implementation overhead.

Buy for repeated constraints

If larger memory changes a weekly workflow, it can justify more budget. If it serves a quarterly experiment, remote capacity or a later upgrade may be more efficient.

• 16 GB and 32 GB are materially different envelopes.
• GPU memory is not system memory or storage.
• Software support can matter as much as capacity.