Quantization

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Quantization is a model compression method that makes neural networks smaller and faster by reducing the precision of the numbers they use. Instead of storing every weight as a 32-bit floating value, the model can use lower-precision formats like 8-bit integers. This cuts memory use and makes models easier to run on mobile and embedded devices. Quantization can be applied after training (post-training quantization) or simulated during training (quantization-aware training), with the latter usually giving better accuracy.

Quantization is widely used because it lowers latency and power consumption with only a small accuracy drop when done well. Teams typically evaluate quantized models on real hardware, compare performance before and after the change, and sometimes combine quantization with pruning or distillation for even better efficiency. It’s one of the most practical techniques for deploying AI in resource-constrained environments.

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