AI Workloads on Private Cloud

Why should you use Private AI

Running AI on private infrastructure or a private cloud offers several key advantages that are critical for modern AI deployments. First and foremost, it ensures data privacy by keeping sensitive datasets fully under your control. Private AI environments also deliver consistent performance and predictable costs, eliminating the variability and surprise expenses common in public cloud AI services. By reducing security exposure and providing secure development environments, private infrastructure helps teams build and train models with confidence. Additionally, private clouds allow for custom model training and optimization, ensuring your infrastructure fits your unique AI requirements. With consistent latency and uptime and no vendor lock-in, Private AI empowers organizations to scale and innovate on their terms.

Learn: vGPUs with OpenStack Nova

This session by Jacob Hipps, at an OpenInfra Summit, provides an in-depth look at vGPU use-cases and best practices with OpenStack Nova, including guidance on when vGPUs are a good fit—and when they may not be the right choice. As a core component of OpenStack, Nova handles VM provisioning, resource management, and access control, making it essential for managing virtual machines in a cloud environment.

The video covers hardware requirements, server and software configuration, and walks through the process of spinning up a vGPU-enabled VM. By the end, you’ll have a clear understanding of vGPU deployments with OpenStack Nova, along with the technical setup and key factors to consider.