// HACKER NEWS — CYBERSECURITY
Show HN: Lumabri – Run Moe Models on a P2P Swarm with Colibri
Run huge mixture-of-experts models from a swarm of peers, with the
colibri engine. Pure C, no dependencies.
One machine shares a model. Any other machine chats with it. Nothing is
downloaded up front: the bytes an inference actually touches arrive from a peer
on first use and stay in a local mirror, so the second question is served from
local disk at full speed. The engine binary is never modified.
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-for-byte the
same either way. Networks that pool GPUs recruit from the few. lumabri recruits
from everyone.
On the machine that has a model (any colibri model directory):
On a machine that wants to chat (it needs a colibri build for the engine):
That is all. The first answer is slower while the working set crosses the
network. Afterwards the mirror in ~/.lumabri keeps serving even if the server
goes offline.
No model at hand? make fixture builds a tiny synthetic one so every step above
is real, just small.
No arguments. It asks for the swarm address and, once, for the operator public
key, finds the engines itself, and remembers all of it in ~/.lumabri/config.
The second time it is Enter, Enter, and you are in. Flags still win when you give
them, so a script never inherits somebody's saved answers.
Inside the chat, /swarm shows the network live and anonymous (peers are
numbered, never named), and /model lists the models on the swarm and switches
between them on the fly.
Sharing bytes. serve runs two small programs: a tracker, which is only an
index of who holds which files, and a maintainer, which answers byte-range reads
on the model directory. A maintainer can hold a slice of a model, and several
maintainers can share one.