In a two-part series, Modular details improvements made to the MLIR (Multi-Level Intermediate Representation) compilation stack, focusing on the development of large models. Key enhancements include the introduction of a "Resources" mechanism to manage constant data like weights more efficiently, separating them from MLIR attributes to improve memory management and development velocity. The team also developed a new binary encoding format for MLIR to replace the previous textual format, significantly increasing the speed of serialization and reducing memory usage during model compilation. These changes resulted in faster model compilation and serialization processes, improving both productivity and execution speed, particularly when compiling models from TensorFlow to runtime input formats. The advancements have been contributed to the LLVM/MLIR repository, benefiting the broader AI ecosystem, and Modular has become a Platinum Sponsor of LLVM, emphasizing its commitment to supporting the growth of the LLVM community.