My Trial-and-Error Log Running Local LLMs at Home (7B→32B)
I couldn't bring myself to upload company documents to the cloud because of privacy concerns, so I'm sharing my trial-and-error process with local models. Bottom line: I'm actually using it now.
At first I ran a 7B model on my laptop, but the answers were too shallow. It could summarize documents but couldn't reason. So I moved up to 13B, which was somewhat better, but as context got longer it slowed down and hallucinations increased.
In the end, I got a used server and moved to running a quantized 32B model.
- GPU memory is the key. A 32B Q4 model runs at around 24GB.
- CPU inference is possible, but the speed is frustrating, so it's not practical for daily use.
- The llama.cpp family was the easiest to set up.
- Adding RAG significantly improves perceived quality. Structuring documents matters more than just scaling up the model.
Cost-wise, excluding electricity, the initial investment payback is about 1–2 years? For personal use, cloud APIs are actually better value. In my case, regulations forced me down this path. If you're going to take this route, calculate GPU memory first.
8 answers
For a 32B Q4, 24GB is tight on a single 4090—I get why you ended up going with a server lol
Well, a 1–2 year payback estimate for personal use seems a bit optimistic. If you factor in the upkeep costs of a used server plus the labor cost of the time spent troubleshooting every time it breaks down, forget payback—it keeps running at a loss. Unless it's a case where regulations force your hand, honestly, using an API is the right call.
Do you have a source? How did you calculate the electricity bill? If you leave it running all the time in idle mode, that can add up too.
I tried integrating something similar at work too, and honestly, structuring the RAG documents is 80% of the battle. Instead of spending money and time scaling up the model, cleaning up chunking and metadata makes a much bigger difference in practice. For us, 13B is enough, so I think we’ll maybe move to 32B later.