On-prem AI means running models (LLM, RAG) on your own infrastructure instead of the public cloud. It makes sense when data is sensitive, covered by NIS2/NDA, or when predictable volume makes owned hardware cheaper than a subscription. Key decisions: deployment model (bare-metal/colo/appliance), GPU selection, and real TCO.
This cluster gathers everything you need for the “on-prem or cloud” decision for AI in European manufacturing, no marketing, just numbers. We cover three deployment models (DIY, productized, managed) and who actually maintains them; the difference between bare-metal, colocation, and appliance in terms of CAPEX and OPEX; and how to calculate TCO so it includes the items most often omitted: staff cost (FTE), energy, cooling, and real, not peak, GPU utilization. We explain card selection by model size (from an ~8B assistant to a production ~70B RAG) and why the inference engine choice (vLLM vs Ollama) can change throughput more than tenfold. We also treat on-prem as a supply-chain risk-reduction tool under NIS2: data never leaves the perimeter. If you want specifics instead of “sovereignty” slogans, start here, then run your case through the TCO calculator.
"On-prem AI" isn't one deployment model but at least three, with different cost, risk, and team-load profiles. We break them down so CISOs and CIOs know which conversation they're really having before the RFP.
Bare-metal in your own server room, colocation with dedicated hardware, or a vendor's managed appliance. Three on-prem AI deployment models for European manufacturing in 2026: CAPEX and OPEX numbers, NIS2 risk profiles, when each makes sense — and when to skip on-prem entirely.
Architecture, GPU sizing, security, integrations, TCO, build vs buy. A practical guide to deploying on-prem AI for CISOs and CIOs in European manufacturing in 2026.
With predictable, steady load and a 2–3 year horizon, that's when CAPEX + energy + FTE can be lower than a rising subscription/token bill. For irregular, low volume, cloud usually wins.
Simplified: ~8B fits on a single L40S/RTX-class card; 70B at reasonable throughput usually needs 2× H100-class (depending on quantization and throughput).
Not automatically, it reduces supply-chain risk (no data transfer), but you still have to implement controls (logging, access, incident response).
Want to apply this to your case: architecture, compliance, and cost?
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