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.
The H200 rarely arrives on demand, and the rollout decision does not wait for delivery. We break down GPU selection for a 70B model from the availability side: how much memory it eats per precision, what it fits on after FP8 and INT4 quantization, and which card to take instead of the H200.
"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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