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title: README
emoji: 🛡
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short_description: Own the model. Own the cloud it runs on.
thumbnail: https://continker.ai/og-image.webp
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<p align="center">
<img src="https://continker.ai/og-image.webp" alt="Continker" style="max-width: 60%; height: auto;">
</p>
<h1 align="center">Own the model. Own the cloud it runs on.</h1>
<p align="center">Continker is an Amsterdam studio building sovereign AI cloud platforms, applied AI, and local models that run offline. Infrastructure organizations own and run themselves, on European terms.</p>
<div class="grid lg:grid-cols-3 gap-x-4 gap-y-4">
<div class="border rounded-lg p-4">
<h3 class="font-semibold mb-1">Sovereign AI cloud platforms</h3>
<p>The infrastructure layer organizations own and run themselves. Model serving, retrieval, and agent workflows on hardware the customer controls, with audit and air-gap built in.</p>
</div>
<div class="border rounded-lg p-4">
<h3 class="font-semibold mb-1">Applied AI</h3>
<p>The systems that run on it. Language-model agents, retrieval, and automation built for real operational work.</p>
</div>
<div class="border rounded-lg p-4">
<h3 class="font-semibold mb-1">Local models</h3>
<p>Small, fine-tuned models that run offline on commodity hardware, so the model layer is owned too.</p>
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</div>
Sovereignty here is technical, not geographic. It is about who controls the keys, the runtime, and the operators, not where the data sits.
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## Open work
MetroLLM-Bench is one example. It is a benchmark and a set of open-weight students for running a transit kiosk from a prose prompt. A small local language model handles the task offline on commodity hardware, and the 2.6 GB student matches a frontier cloud API.
| Model | Size | Runs on |
|-------|-----:|---------|
| [Qwen3.5-2B-metro-v24](https://huggingface.co/continker/Qwen3.5-2B-metro-v24) | 1.2 GB | anything, including CPU-only |
| [Qwen3.5-4B-metro-v24](https://huggingface.co/continker/Qwen3.5-4B-metro-v24) | 2.6 GB | a 16 GB laptop |
| [Qwen3.5-9B-metro-v24](https://huggingface.co/continker/Qwen3.5-9B-metro-v24) | 5.3 GB | a 24 GB machine |
| [Qwen3.5-27B-metro-v24](https://huggingface.co/continker/Qwen3.5-27B-metro-v24) | 16 GB | a 32 GB machine |
[Try the live demo](https://huggingface.co/spaces/remcohendriks/metrollm) · [Browse the collection](https://huggingface.co/collections/continker/metrollm-bench-v24-6a35b586a11068e1b1ba3d47) · [Benchmark and code](https://github.com/continker/metrollm-bench)
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More at [continker.ai](https://continker.ai).
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