Yash Nankani developed a mixed-precision quantization feature for the hpcaitech/TensorRT-Model-Optimizer repository, focusing on enabling configurable accuracy and performance trade-offs for machine learning model deployment. Using Python and leveraging data processing and quantization techniques, he implemented support for INT4 and INT8 quantization strategies, allowing users to specify 8-bit layers via new command-line options. His work included enhancements to precision mapping and scaling functions, which improved inference throughput and reduced model size for edge and server environments. The feature was delivered with validated tests, demonstrating a solid understanding of quantization and deployment challenges in resource-constrained scenarios.