Honest take on running 9× RTX 3090 for AI

[my home server](https://preview.redd.it/ry0d887xamqg1.jpg?width=3000&format=pjpg&auto=webp&s=0a8e456e366c5c31ba62a1c1523dd547015b37b3) [3090 4way](https://preview.redd.it/r2p54vsvamqg1.jpg?width=4000&format=pjpg&auto=webp&s=bed6026c8ff57a8c7526641995bceccdb23e4c62) I bought 9 RTX 3090s. They’re still one of the best price-to-VRAM GPUs available. Here’s the conclusion first: 1. I don’t recommend going beyond 6 GPUs 2. If your goal is simply to use AI, just pay for a cloud LLM subscription 3. Proxmox is, in my experience, one of the best OS setups for experimenting with LLMs To be honest, I had a specific expectation: If I could build around 200GB of VRAM, I thought I’d be able to run something comparable to Claude-level models locally. That didn’t happen. Reality check Even finding a motherboard that properly supports 4 GPUs is not trivial. Once you go beyond that: • PCIe lane limitations become real • Stability starts to degrade • Power and thermal management get complicated The most unexpected part was performance. Token generation actually became slower when scaling beyond a certain number of GPUs. More GPUs does not automatically mean better performance, especially without a well-optimized setup. What I’m actually using it for Instead of trying to replicate large proprietary models, I shifted toward experimentation. For example: • Exploring the idea of building AI systems with “emotional” behavior • Running simulations inspired by C. elegans inside a virtual environment • Experimenting with digitally modeled chemical-like interactions Is the RTX 3090 still worth it? Yes. At around $750, 24GB VRAM is still very compelling. In my case, running 4 GPUs as a main AI server feels like a practical balance between performance, stability, and efficiency. (wake up 4way warriors!) Final thoughts If your goal is to use AI efficiently, cloud services are the better option. If your goal is to experiment, break things, and explore new ideas, local setups are still very valuable. Just be careful about scaling hardware without fully understanding the trade-offs.