Quantization and model compression reduce the size and complexity of AI models by simplifying how data and parameters are stored and processed. This makes models smaller and faster without severely impacting accuracy.
Quantization converts model parameters from high-precision numbers (like 32-bit floats) to lower precision (like 8-bit integers), cutting memory use and speeding up computation. Compression techniques remove redundant information and optimize the model structure to reduce storage and improve inference efficiency.
For AI product managers, these techniques lower hardware costs, reduce latency, and enable AI to run on edge devices. This improves user experience and scalability, allowing deployment in resource-constrained environments while maintaining performance.