Explore real-world solutions to AI deployment challenges—from managing secure, container-based environments to scaling GPU infrastructure efficiently. Learn how Kasm Workspaces and OpenMetal enable secure collaboration, cost control, and streamlined experimentation for AI teams.
Tag: AI workloads
This article highlights OpenMetal’s perspective on AI infrastructure, as shared by Todd Robinson at OpenInfra Days 2025. It explores how OpenInfra, particularly OpenStack, enables scalable, cost-efficient AI workloads while avoiding hyperscaler lock-in.
At OpenMetal, you can deploy AI models on your own infrastructure, balancing CPU vs. GPU inference for cost and performance, and maintaining full control over data privacy.
When it comes to raw performance metrics, GPUs often lead the pack. Their architecture is specifically tailored for the high-speed, parallel processing demands of AI tasks. However, this doesn’t always translate into a one-size-fits-all solution.
In this article, we’ll explore scenarios where CPUs might not only compete but potentially gain an upper hand, focusing on cost, efficiency, and application-specific performance.