AirCloud got a major update in June 2026. This release focused on improving the experience for every team — whether you're just getting started with AirCloud or running multiple projects every day. We redesigned the console, introduced real-time notifications, and expanded persistent volume support to cover more deployment environments. The goal was to make the platform feel faster and more intuitive at every step. For detailed usage guides, visit AirCloud Docs. This post cov

The Distributed GPU Cloud Story and Why Ray Is at the Center of It 💡 Core Message "A GPU that never serves a request has no business value. " Air Cloud connects everything from the runtime layer to the platform layer, so hardware actually reaches users as a real service. Introduction When people talk about AI infrastructure today, the conversation usually starts with GPU scarcity. How many H100s did you lock in? Is B200 supply going to loosen up? Does your data center have e

AirCloud's April release is built around one goal: making it faster to run AI workloads, more reliable to operate them, and more flexible to put your existing GPU resources to work. This update includes enhanced Air Container operations, the general availability of Air API, Resource Provider (RP) support, and the introduction of an intelligent scheduler. Developers can now handle container access, log monitoring, error response, and API integration more seamlessly. Enterprise

If you’ve ever tried to train a large AI model or run complex simulations, you know how crucial GPU power is. But buying and managing physical GPUs can be a headache. That’s where distributed GPU time comes in. Imagine tapping into a vast network of GPUs spread across the cloud, paying only for what you use, and scaling effortlessly. Sounds like a dream, right? Well, it’s not just a dream anymore. In this post, I’ll walk you through everything you need to know about how to pu