Large language models (LLMs) often demand hand-coded conversion scripts for deployment on each distinct processor-specific software stack—a process that’s time-consuming and prone to error. In this session, we introduce a model-agnostic approach designed to streamline LLM deployment, especially for the NVIDIA GPUs. We’ll demonstrate how our automated approach cuts through the complexity of constantly evolving software stacks, enabling faster, more reliable LLM adoption. Attendees will gain practical insights into a future-proof strategy that boosts coverage for new and upcoming LLM architectures, all while reducing manual coding effort.