How to Self-Host Aleph Alpha's Kolibri 1 on a European Sovereign Cloud with Kubernetes

Aleph Alpha’s Kolibri 1 is presented as an Apache 2.0-licensed, open-weight 78.1-billion-parameter mixture-of-experts model that can be commercially self-hosted, with 3.46 billion parameters active per token and fp8 weights requiring roughly 78 GB of GPU memory before KV-cache overhead. The guide argues that organizations seeking European data sovereignty should distinguish EU-owned or EU-operated cloud providers and dedicated sovereign-cloud offerings from ordinary European regions of US hyperscalers, which it says may remain exposed to the US CLOUD Act. For production deployment, it recommends Kubernetes with GPU node pools, the NVIDIA GPU Operator, vLLM with tensor parallelism, persistent Hugging Face weight caching, shared-memory configuration, TLS, and API authentication, typically using two H100 GPUs or one H200 for the published fp8 model. It emphasizes that GPU inference economics depend chiefly on utilization and idle capacity, advising queue-based autoscaling, cached weights to reduce cold starts, scale-to-zero for nonproduction environments, and benchmarking of latency and throughput before capacity commitments. The article also positions Qovery as a bring-your-own-cloud platform that can manage Kubernetes operations in an organization’s own account while leaving model serving to vLLM, and concludes that self-hosting is most compelling for high-volume workloads or strict control and jurisdiction requirements rather than lightly used deployments.

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