Quantization is a cornerstone of modern AI systems, enabling neural networks to perform inference and training efficiently without sacrificing significant accuracy. Within PyTorch, Quantization Operators form the core of this optimization strategy, offering tools for quantization-aware training (QAT), post-training quantization (PTQ), and seamless deployment using Just-In-Time (JIT) compilation and TorchScript. This paper systematically unpacks the … Continue reading Quantization Operators in PyTorch: The Foundation of Modern AI Optimization with QAT, JIT, and TorchScript