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The original was posted on /r/homelab by /u/Obvious_Service_8209 on 2025-07-31 14:52:13+00:00.
Just wanted to share something I’ve been quietly building — it started as a scheduler for AI workloads, but I think it might have broader implications.
A lot of us buy affordable GPUs (A2000s, 3060s, even 1050Tis) and stick them into a homelab setup — but no one’s really talking about software-level optimization.
I built a module that:
Monitors GPU/CPU/memory use
Uses ML to learn from your own workload patterns
Allocates tasks (inference, background jobs, etc.) to the best device over time
Falls back to CPU intelligently (especially if you're working with huge RAM pools)
Result?
My dual A2000 + Threadripper Pro + 128GB ECC rig outperforms a single 3090 in many AI and inference jobs, especially over longer batch sessions.
It’s kind of like “Run:ai for homelabbers.” No Kubernetes. No fluff. Just a lightweight allocator that learns how to use your machine better.
Thinking about open-sourcing it. Curious if anyone else would use something like this — or already built their own hacky version?
Thanks!