Ilya Amursky

Hi everyone, I'm Ilya. I'm building Hinode, a GPU cloud with a persistent Linux workspace for people building and running AI.

You set up apps and dependencies in your workspace, save a snapshot, and launch GPU machines from that setup. Models, datasets and outputs live in a shared folder that all your machines can access. Pausing a machine preserves its environment, so you can pick up where you left off.

Hinode supports workflows such as ComfyUI, model training and batch inference. You can work through a desktop, SSH or VS Code, or connect an AI assistant through MCP to start machines and run jobs.

If you're building with open models, which part of your GPU workflow takes the most effort today: environment setup, moving data, or managing compute?

Here's Hinode: hinode.run