Every few weeks a take goes viral in tech circles making the case for ditching cloud AI and running models locally. The argument is always roughly the same: cloud costs add up, your data is being shipped to American servers of dubious legal standing, and a one-time GPU purchase pays for itself in 18 months. Bold claim. Simple math. Lots of hashtags.
It deserves a closer look.
The typical version of this argument runs something like: two RTX PRO 6000 Blackwells, 1,200W draw, six hours a day, β¬0.32 per kWh β "about β¬48/month" in electricity. The cards themselves cost around β¬16,000. Cloud AI, by comparison, runs β¬100β200 per developer per month. Eight developers, 18 months, done.
Except the electricity bill is already wrong. 1.2 kW Γ 6h Γ 30 days Γ β¬0.32 = β¬69.12. Not β¬48. A 44% error in the opening calculation of an argument whose entire appeal is rigorous arithmetic.
The break-even math has bigger problems. β¬100β200/month per developer implies roughly 20 million tokens consumed per person per month. That is not a power user. That is a token foundry. For any team using AI at normal human rates, the break-even slides quietly past two years β by which point the GPU generation is already dated.
The β¬16,000 hardware figure also never travels with:
- Cooling. 1,200W sustained is a serious heat load. Office HVAC was not designed for this.
- Labor. Keeping local model infrastructure running β version management, security patches, prompt compatibility across model updates β is real engineering work that doesn't appear in these spreadsheets.
- Hardware failure. Cloud providers have SLAs. Your server closet does not.
Noise. Two RTX PRO 6000 Blackwells under full load exceed 50 dB β a loud dishwasher, sustained, all day. In a dedicated server room, fine. In a shared office, your colleagues will have opinions.
Availability. The RTX PRO 6000 Blackwell is a new, high-demand professional card with constrained supply and multi-week lead times. If one card fails, you are not buying a replacement over the weekend. You wait β potentially a month or more. Keeping a spare sounds prudent; that spare costs another ~β¬8,000 and is equally hard to source. A single-point-of-failure setup with no redundancy and a six-week replacement window is not infrastructure. It is optimism.
Where the Argument Has a Point
Data sovereignty is real. GDPR compliance for third-country data transfers is genuinely complex, vendor terms change, and strategic dependence on external model providers is a risk that tends to get underweighted until it isn't. The upfront capital requirement is the actual barrier for most teams, not the long-run economics.
But the most important question gets skipped entirely: is the local model actually as good? Two Blackwells with 192GB VRAM can run serious open-weight models β this is not a toy setup. But if developers need two or three attempts to get what a frontier cloud model produces in one, the labour savings evaporate and the break-even never arrives.
The Bottom Line
Local AI infrastructure can make sense β for teams with heavy, sensitive workloads, strong in-house ops capability, and the capital to do it properly, including redundancy, cooling, and the realistic assumption that hardware will occasionally fail at inconvenient times.
What it is not is a simple 18-month arbitrage available to anyone with a GPU and a spreadsheet.
The sovereignty argument is the strongest card in the deck. Lead with that. The cost argument needs a lot more columns in the spreadsheet before it holds up.
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