GoKawiilHetzner is experimenting with LLM inference.
That is not a sentence I expected to write, but I think it is pretty interesting :)
Before anyone moves their production AI workloads to Hetzner: this is very much an experiment. There is no billing, no SLA, no production guarantee, and currently only one model. Hetzner says it wants to learn whether people actually want this, how the system scales, which features matter, and what kind of load it can handle.
So this is not a finished product launch. It is Hetzner putting something early in front of users and seeing what happens. I really like that approach.
Hetzner Inference is an OpenAI-compatible API running on Hetzner's own infrastructure. You create an API token in the Experiments dashboard, point an OpenAI client at Hetzner's base URL, and use it like most other inference APIs.
Right now, the only available model is Qwen/Qwen3.6-35B-A3B-FP8 . It is a 35-billion-parameter Mixture-of-Experts model with 3 billion active parameters. It accepts text and images, has a 262K context window, and uses FP8-quantized weights.
That is a perfectly reasonable model for an experiment. It is small enough to serve without a ridiculous GPU cluster, but still useful enough to test the API with real workloads.
Hetzner also published a short tutorial for connecting OpenCode to the API, if you want to try it without writing any code.
Because the API is OpenAI-compatible, there is almost nothing special about the integration:
pip install openai
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