NVIDIA's TensorRT Model Optimizer Brings QAT and QAD Techniques to Recover AI Accuracy Lost in Quantization
NVIDIA's TensorRT Model Optimizer introduces QAT and QAD techniques that recover AI accuracy lost during quantization by simulating low-precision arithmetic during training, with QAD leveraging a full-precision teacher model to guide quantized models, delivering benchmark improvements on models like Llama Nemotron Super via PyTorch and Hugging Face-compatible APIs.