To virtualize or not to virtualize AI Infrastructure: A perspective

Abstract

Modern data-driven applications (such as AI training, Inference) are powered by Artificial Intelligence (AI) infrastructure. AI infrastructure is often available as bare-metal machines (BMs) in on-premise clusters but as virtual machines (VMs) in most public clouds. Why is this dichotomy of BMs on-prem and VMs in public clouds? What would it take to deploy VMs on AI Systems while delivering baremetal-equivalent performance? We will answer these questions based on experiences building and operationalizing a large-scale AI system called Vela in IBM Cloud. Vela is built on open-source Linux KVM and QEMU technologies where we are able to deliver near-baremetal (within 5% of BM) performance inside VMs. VM-based AI infrastructure not only affords BM performance but also provides cloud characteristics such as elasticity and flexibility in infrastructure management.