Quantization reduces the precision of the numbers inside a model, for example storing weights as 8-bit or 4-bit values instead of 16-bit. This makes the model smaller in memory and faster to run, often with only a small drop in accuracy. It is one of the main techniques for making large models practical on cheaper hardware.

It matters because model size drives infrastructure cost and speed. A quantized model can fit on smaller, less expensive GPUs, respond faster, and even run on edge devices. For many production workloads, the slight quality trade-off is well worth the large savings in cost and latency.

At arosplatforms we use quantization to right-size deployments, especially where a client wants strong performance without frontier-model infrastructure bills. We test quantized models against the original on real tasks so any accuracy change is measured, not assumed, before going live.