I tried to connect every GPU on the planet together (kinda)

Nowadays, unless you’re training a frontier model from scratch, finding spare GPU capacity for training and post-training on some cloud is manageable.But a lot of the time, I’m just too lazy to move my work or even go through the signup processes and deal with their limits and regions and shit. So I wasted a lot of money keeping GPUs idle or gambling on a paused instance, crossing my fingers that the node would still be available when I wanted it again (usually didn’t work).So here’s my journey trying to automate this process, and it was a lot harder than you might think.1. Shared File System“Just mount a network filesystem… duhh.” Well, not so fast. Many providers have random restrictions and sandboxed compute; the RunPod containers I tested, for example, didn’t expose FUSE. To build a universal solution, I needed another way.I built a custom LISAFS adapter and, over months of refinement, got it to the point where it outperforms even network filesystems through clever syncing: packing small files into fewer transfers, fetching file ranges on demand, and using a Rust-based storage engine to reuse cached content-addressed blocks instead of copying whole files. The system works so…