Quanto: a PyTorch quantization backend for Optimum

Optimum has introduced Quanto, a new PyTorch quantization backend designed to enhance model performance and reduce memory usage during inference. Quanto supports post-training quantization and provides tools for both dynamic and static quantization approaches, allowing practitioners to optimize transformer models efficiently. This release is significant for AI engineers as it facilitates the deployment of large models on resource-constrained environments without substantial accuracy loss.