Quantisation

Plain English. Storing a model's weights at lower numerical precision — fewer bits per number — so the same model takes a fraction of the memory and runs on smaller, cheaper hardware, at the cost of some accuracy. Think of it as compression for models: a 4-bit build of a model can be around a quarter the size of the 16-bit original and remain usable.

Why it moves money. Quantisation is the mechanism that moves near-frontier capability out of the data centre and onto hardware a customer already owns. Every step down in precision widens the set of machines a given model fits on, which strengthens the local-inference alternative to metered APIs and shrinks the hardware bill for anyone serving open-weights models. It converts a lab's headline release into something that runs on a laptop within days.

What to watch. Measured accuracy retention at aggressive precision (independent numbers, not the quantiser's), and whether labs ship quantised builds on day one — that is a distribution strategy, not an afterthought.

From the signals. Qwen3.8-27B shipped an FP8 quantised build within hours of release. GLM-5.3's 2-bit build is 83% smaller at roughly 81% top-1 accuracy, on Unsloth's documentation, and fits on a Mac Studio. A 17GB quantised build scored 52 on Artificial Analysis and ran on a MacBook Pro.