This article follows I Tried Kimi K3 Inside Claude Code. That test asked if Kimi K3 could work inside a familiar coding setup. Here, I ask what changes when a Swiss provider runs a K3-class model for customers that need local control.
Banks already treat data sovereignty as an operating issue. In How DORA Made Sovereignty a Bank Problem, I argued that banks must plan for concentration, exit, audit rights, and rule changes by critical foreign providers.
AI gets the same treatment. Recent open-weight releases make local use more realistic. Kimi K3 is huge. Alibaba is moving the Qwen family in the same direction. Inkling may matter more. It is a US-trained model with full weights, a one-million-token context, and a focus on customisation.
Assume a Swiss provider operates one 64-GPU cluster in Zurich. It sells dedicated or managed K3-class inference to banks, pharmaceutical companies, government bodies, and other customers that need Swiss data residency.
This case assumes $7 million in initial costs, monthly costs of $370,000, and average use of 70%. Subscriptions cost CHF 15,000 to CHF 50,000 each month. With these values, one cluster makes about CHF 7.4 million a year. Its EBITDA is about CHF 3 million. The payback period is three years.
The provider sells control as well as tokens. It offers Swiss residency and a defined security boundary. It does not train on customer data. It gives customers audit access, continuity terms, and an exit route if it fails.
Most companies will not put a model in a basement. Some will pay local providers to get the same control.
A 2.8-trillion-parameter model costs a lot to serve. One cluster creates a concentration risk. Hardware ages fast. If use falls, the case loses money. A smaller model may work almost as well next year. Then the hardware loses value before the operator recovers its cost. I still think this market will exist.