Lumabri experiment aims to run large AI models across peer-to-peer networks

A developer has launched Lumabri, an experimental project that distributes the workload of running large language models across multiple ordinary computers in a peer-to-peer network. Inspired by earlier work on Colibrì, which explored running large LLMs on a single consumer machine, Lumabri treats a swarm of networked computers as a shared pool of disk, compute, and model resources. The system is particularly suited to Mixture-of-Experts models, where only a fraction of parameters are activated per token, allowing small activations to be sent to peers that already hold the relevant model weights. The developer is experimenting with security and reliability features including SHA-256 verification, signed model state, peer verification, and failover mechanisms. Lumabri is an early-stage personal experiment built without datacenter infrastructure, and the source code is publicly available on GitHub.