AI workflows on HPC systems need different optimization strategies than traditional scientific computing: focus on containerization for portability, smart job scheduling, explicit feedback mechanisms, and I/O efficiency rather than just raw compute throughput.
This guide offers twelve practical strategies for running AI workloads efficiently on HPC clusters. It addresses the unique challenges of AI workflows—which are iterative and data-driven—compared to traditional scientific computing, covering containerization, job scheduling, feedback loops, and file I/O optimization to help researchers build scalable, reproducible AI pipelines.