In the first installment of a two-part series, the challenges and solutions surrounding the development and deployment of large AI models are explored, particularly focusing on the inefficiencies and complexities in current tooling that hinder productivity. As machine learning models grow in scale, they present significant difficulties for existing AI infrastructure, especially in terms of handling massive weights, which can reach over 100 gigabytes. Modular, a company dedicated to enhancing developer productivity, addresses these challenges by optimizing their toolchain and leveraging the Multi-Level Intermediate Representation (MLIR) compiler framework. MLIR, part of the LLVM project, offers a modern and extensible approach to building domain-specific compilers, yet its traditional handling of large data presents issues such as inefficient memory allocation and serialization. To mitigate these, Modular has introduced core additions to MLIR that include efficient memory mapping, avoiding unnecessary data hashing, enabling inline mutations, and ensuring fast serialization. These improvements not only enhance developer workflows by reducing complexity but also contribute to the broader MLIR community, as Modular actively participates in driving its evolution.