In an October 2 post discussed on r/LocalLLaMA, Bas Nijholt said he runs Qwen3.8 27B on two RTX 3090 cards without expecting to save money.
His calculation found that electricity above 14.5 cents per kWh alone exceeded the GPT-6 Luna API bill. Including the card purchase costs, breaking even over three years required four busy hours every day with free power or 8.5 hours at 18 cents per kWh.
Against an API serving the same Qwen3.8 27B, self-hosting was cheaper. The conclusion changed with the chosen API comparison, not with the physical hardware.
A later addition covered 128 GB Macs, DGX Sparks and AMD AI Max devices. His estimated hardware payback remained six to ten years even with free electricity.
Hardware costs and unmatched model quality
Electricity-only accounting may fit hardware already owned. A new purchase brings capital cost, daily utilization and maintenance into the calculation.
Output quality was not directly matched either. Nijholt said he did not rerun the comparison benchmark on his quantized local model and instead assigned it an API-measured Qwen score.
The Reddit comments could not be checked directly at the earlier review. These are the author's calculations and a public discussion, not an independently reproduced same-task cost test or a consensus of users.
What a workload-specific comparison needs
A practical comparison starts by giving local and API systems the same task and tracking completion rate, latency and retries. Hardware price, electricity rate and hours of use belong in the same sheet.
Privacy, offline operation and model control can still justify self-hosting at a higher cost. Nijholt also cites enjoyment and control as reasons to continue.
The economics of a local LLM depend on utilization, the API baseline and successful work—not on GPU cost alone.