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This article addresses critical infrastructure challenges for AI workloads by proposing Unikraft's sandboxing technology to achieve high-density scaling and millisecond cold boots within Kubernetes environments. The focus remains strictly on the computational efficiency and isolation primitives required to support massive AI model training and inference clusters, rather than general software or consumer applications.
Presentation: Fixing the AI Infra Scale Problem by Stuffing 1M Sandboxes in a Single Server
Felipe Huici explains how Unikraft achieves millisecond cold boots, stateful scale-to-zero, and extreme density for sandboxing AI workloads. He discusses isolation primitives, Linux kernel optimizations, and snapshotting tricks, demonstrating how to maintain sub-10ms performance at scale while integrating seamlessly into Kubernetes environments with hardware-level security. By Felipe Huici
Felipe Huici: My name is Felipe. I'm the CEO and co-founder at Unikraft. We build cloud platforms or we allow people to build cloud platforms on our software, that hopefully scale to a lot of things where security is pretty good and where VMs fly. That's the idea anyways. Let's see how far we got there. This talk obviously is going to be focused a little bit more on sandboxes because that's the cool thing. Sandboxes used to be a container primitive. These days it's about all AI. The term has been repurposed if you've been around long enough, but we'll get to that. To get started, how fast can a virtual machine cold boot? Anybody want to take a guess? I put some options. I said I have time for lunch, I have time for coffee, a few seconds, and a few milliseconds. Of course, as I'm trying to preach that they can go fast, I'll say a few milliseconds and then we'll see if it's true. Another one, how many virtual machines can I put in a 48-core server? I wrote, 1 Ubuntu-based one, 10s of them, 100s if I'm feeling adventurous, or if I could somehow manage…
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