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Model too big for your GPU, budget too small for the model.
What you'll have at the end
A quantized checkpoint with a real before-and-after score on the same test, showing exactly how many points of accuracy it cost you to fit your hardware.
You need
A trained checkpoint (a saved copy of the model's weights) you can already load and run at full precision, plus a real GPU memory budget in mind.
Not covered
Deciding whether to fine-tune a smaller model instead of quantizing this one; that choice happens before this recipe starts, which assumes you already know which checkpoint you're keeping.
Leans on
Work out your app's cost per request before you build the rest
redo that cost estimate once you have the quantized checkpoint's real memory footprint and speed, since both change the number
Put every model call behind one gateway that routes, caches, and falls back
route production traffic to the quantized checkpoint through a gateway that can fall back to the full-precision one when a request needs the accuracy back
Checked 15 Aug 2026
Part of the AI Engineering cookbook