From Public Cloud AI to National Infrastructure: Lessons from Sunet Scribe#

Erik Lundberg (Sunet)

• Why: A rare, fully-costed-in-public view of running a national AI inference service on a small GPU fleet — the operational reality behind "sovereign AI services."
• What: Sunet Scribe, a self-hosted, Whisper-based speech-to-text service inside the Swedish NREN, born because institutions didn't trust commercial providers with sensitive recordings and commercial STT wasn't optimized for Nordic languages (the fine-tuned Swedish model comes from the Royal Library). Timeline: community request mid-February 2026 → prototype and first GPU machines in May → institutional pilot October 1 → national launch December 1. Traction: 30 universities, ~3,000 users, ~650,000 minutes transcribed in 2026 (3–4× the 2025 baseline) — handled by three GPU servers.
• How: Deliberate architecture choices: self-hosted with federated login, zero-access model, automatic retention; integrated with Kaltura (and NextCloud in 2026) with a published API; open-source, modular, with swappable models and components — consolidating what had been parallel commercial/secure/internal STT pipelines into one operational platform.
• Where applicable: Nordic-language and trust context is specific, but the pattern — small GPU fleet, open models, platform integration, one year from request to national service — is a transferable template for institutions weighing public-cloud AI vs. self-hosted.