This presentation showcases the key changes to the SPIR-V backend that have solidified its position as a robust, vendor-agnostic alternative to the Khronos LLVM/SPIR-V Translator. We explore recent feature enhancements, translation time optimizations, and quality assurance processes that enabled seamless integration with other projects for heterogeneous computing workflows, including GPU programming for neural networks. Emphasis is placed on bridging the requirements of the SPIR-V standard, relevant execution environment specifications, and LLVM’s code-lowering infrastructure. Key topics include technical solutions for the SPIR-V type system, module vs. function scope handling, and managing the logical layout of modules—factors essential to both performance and maintainability. We also discuss our use of TableGen-based register classes, improvements to type deduction passes, and how these strategies ensure formal correctness while expanding support for SPIR-V extensions, built-ins, and intrinsics. Finally, the talk covers insights into introducing the backend into the DPC++ compiler and OpenAI Triton backend for Intel GPUs, along with details on the broader quality assurance and integration efforts that benefit both compute-centric and graphical use cases alike.