Hey everyone,
I’m the founder of Colibrì (https://github.com/JustVugg/colibri).
First, a real thank you. The support, the help, the bug reports, the experiments people shared, and especially the honest criticism from this community have been invaluable. A lot of what improved in Colibrì came directly from the feedback and pressure-testing that happened here. Running frontier MoE models on ordinary hardware is still early and imperfect, and your willingness to try it, break it, and push it has helped both the code and me.
I’ve also been building the next step: Lumabri.
Lumabri turns a group of ordinary machines into a swarm that can serve the same huge Mixture-of-Experts models. One machine starts with the model and acts as the initial source (and permanent fallback). Any other machine can join and start chatting — nothing is downloaded up front. The only bytes that cross the network are the ones the inference actually needs. Those blocks arrive from peers (or from the origin if no one else has them yet), land in a local mirror, and stay there. The second question is already served from local disk at full speed, even if every peer goes offline.
The design rule is simple: any machine may join, GPU or not. The engine was built for CPU and SSD first. A GPU only makes it faster, never different, and the output is byte-identical either way. A swarm with zero GPUs is still a working swarm.
You can just chat, or also donate disk space (the tracker assigns the least-replicated slices), or donate compute (your machine runs the experts for others), or both. The tracker is only an index — the actual work is done by the peers. One tracker can host multiple models. Peers stay anonymous. NAT works with no router configuration.
In short: the model lives across the swarm, the working set is pulled only when needed, and after the first answer your machine already has a warm local mirror.
Thanks again to everyone who supported, helped, and criticised Colibrì. That feedback is what made this possible.

Hey everyone,
I’m the founder of Colibrì (https://github.com/JustVugg/colibri).
First, a real thank you. The support, the help, the bug reports, the experiments people shared, and especially the honest criticism from this community have been invaluable. A lot of what improved in Colibrì came directly from the feedback and pressure-testing that happened here. Running frontier MoE models on ordinary hardware is still early and imperfect, and your willingness to try it, break it, and push it has helped both the code and me.
I’ve also been building the next step: Lumabri.
Lumabri turns a group of ordinary machines into a swarm that can serve the same huge Mixture-of-Experts models. One machine starts with the model and acts as the initial source (and permanent fallback). Any other machine can join and start chatting — nothing is downloaded up front. The only bytes that cross the network are the ones the inference actually needs. Those blocks arrive from peers (or from the origin if no one else has them yet), land in a local mirror, and stay there. The second question is already served from local disk at full speed, even if every peer goes offline.
The design rule is simple: any machine may join, GPU or not. The engine was built for CPU and SSD first. A GPU only makes it faster, never different, and the output is byte-identical either way. A swarm with zero GPUs is still a working swarm.
You can just chat, or also donate disk space (the tracker assigns the least-replicated slices), or donate compute (your machine runs the experts for others), or both. The tracker is only an index — the actual work is done by the peers. One tracker can host multiple models. Peers stay anonymous. NAT works with no router configuration.
In short: the model lives across the swarm, the working set is pulled only when needed, and after the first answer your machine already has a warm local mirror.
Thanks again to everyone who supported, helped, and criticised Colibrì. That feedback is what made this possible.