I, like many nerds of the same stripe, have a dragon's hoard of every PC component I've owned in the last 20 years. I've attached as much of it to my homelab as

I, like many nerds of the same stripe, have a dragon's hoard of every PC component I've owned in the last 20 years. I've attached as much of it to my homelab as is practical, but there's still a pile of GPUs from the last decade plus. So I decided to load up everything with more then 3GB of VRAM into various machines on the network. Anything that could conceivably run an LLM of any utility. I've been experimenting with driving a swarm of heterogenous LLMs into coding tasks. I have models as small as llama3.2:3b up to Qwen3.6:27b dense. Over 10 unique models in the swarm. So far, the results are... interesting. Coding isn't great, but what has worked shockingly well is polling the swarm for opinions. Getting ten unique perspectives synthesized into a single summary has been astonishingly useful. When I gave the swarm the ability to debate with itself, the results got even more interesting. The end goal here is an autonomous routing network that learns which models excel at which tasks, which machines can fit which models, and intelligently routes requests and models to where they're most effective. I can't afford an RTX 6000, but I can run smaller models on the pile of GPUs I do have. So far it hasn't worked out the way I'd hoped, but it did turn out to be very useful in other ways. Hopefully soon I can get coding worked out and the swarm can drive itself into self-improvement