Taesu Kim, Chief Technology Officer at SqueezeBits, presents the “Bridging the Gap: Streamlining the Process of Deploying AI onto Processors” tutorial at the May 2025 Embedded Vision Summit.
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 presentation, Kim introduces a model-agnostic approach designed to streamline LLM deployment, especially for NVIDIA GPUs.
Kim demonstrates how his company’s automated approach cuts through the complexity of constantly evolving software stacks, enabling faster, more reliable LLM adoption. You’ll gain practical insights into a future-proof strategy that boosts coverage for new and upcoming LLM architectures, all while reducing manual coding effort.
See here for a PDF of the slides.
