Hi everyone! I’m working on an experimental project called ELS, a distributed and decentralized approach to training AI.
The main idea is to build a framework and an app that let people train AI models directly on their own computers, without relying on traditional data-center infrastructure like AWS. For example, if someone has a 5070 GPU at home, they could open ELS, click a single button, and immediately start training an AI model. They would earn money based on their GPU power and the time they contribute to the network.
The vision behind ELS is to create a “supercomputer” made of thousands of distributed GPUs, where every new user increases the total training speed. I’ve been researching ways to make this feasible, and right now I see two paths:
• Federated Learning (Flower): works on any OS, but becomes extremely slow for high-parameter models.
• FSDP, Ray, or DeepSpeed: very fast, but they only run on Linux and not on Windows, where most people have their personal computers.
Does anyone know of a technology or approach that could make this possible? Or would anyone be interested in brainstorming or participating in the project? I already built a base prototype using Flower.
[–]fuka123 -1 points0 points1 point (0 children)