What this is
This week on Reddit's LocalLLaMA community (the hub for discussing local deployment of open-source large models), user soyalemujica raised a concrete question: running Qwen-series models locally on an AMD 7900XTX paired with a 9800X3D processor, and layering on Europe's €0.25/kWh electricity rate, comes to €0.12 per hour in inference (the model generating responses) power costs. He wanted to know: since APIs like DeepSeek charge per token (the smallest unit of text a model processes) at far lower rates, wouldn't it actually be cheaper to just pay for the cloud API?
The underlying story is that the cost curves of local AI (running large models on your own machine or server) and cloud AI are crossing in 2025. On the surface, local inference only requires paying for electricity and seems always cheaper than pay-as-you-go pricing; but once we factor in GPU depreciation, debugging time, and inference speed differences, the conclusion flips.
Industry view
The cloud camp argues that domestic model APIs from DeepSeek, Qwen, and others have driven prices down to the range of a few cents to a few dimes of RMB per million tokens, already below hardware amortization costs for the vast majority of individual users. Even a user running a local GPU 10 hours a day still can't beat the API on a pure cost basis. Cloud pricing keeps falling, and we expect the gap to widen further.
The local camp offers three counterarguments. First: data stays home. For users handling customer privacy, medical records, or contract drafts, the compliance risk of uploading data to a third-party server via API far outweighs the modest electricity savings. Second: hardware amortization is a hidden cost. A 7900XTX used for three years at 8 hours a day drops its true hourly cost from €0.12 to under €0.05, which means heavy users actually come out ahead. Third: API providers may raise prices, throttle usage, or shut down — betting all of your AI needs on a single vendor isn't a safe position.
There's another layer we often overlook: European electricity runs €0.25/kWh, China's residential rate sits around ¥0.5/kWh (roughly €0.07), and some US states go lower. Individual Chinese users running AI locally may pay only a quarter of what this European user pays. The math comes out differently by region.
Impact on regular people
For enterprise IT: The window for SMBs to self-build AI servers is narrowing. Unless the business has hard data compliance requirements, calling cloud APIs directly is the more rational first step — revisit whether local deployment is worthwhile once usage stabilizes.
For individual professionals: Knowledge workers shouldn't stress about "running large models locally." The marginal return on buying a high-end GPU specifically for AI is declining; DeepSeek's web version or API already covers 90% of daily needs. Time spent learning to ask better questions pays more than time spent wrestling with GPU setup.
For the consumer market: The "mining-boom" narrative around consumer AI GPUs is fading. Once APIs are cheap enough, the only reasons for average users to buy a 4090 or 5090 for local inference are enthusiast tinkering and strong privacy needs. Hardware premiums will keep getting compressed.