Why 60% of AI Will Be On-Prem

Open source and open weight models are the economic engine that makes a 60% on-prem AI mix plausible. If an enterprise wants most inference to stay inside its own data center or edge sites, it needs models it can actually download, run, tune, and meter on its own GPUs. That turns AI from a variable API bill into infrastructure the company controls, while keeping frontier APIs reserved for harder reasoning and overflow demand.

• The practical workflow is simple. A developer points Claude Code, Cursor, or another tool at a local OpenAI compatible endpoint, routine coding or retrieval jobs run on a self hosted open model, and platform rules send only harder requests to external models. That is how hybrid becomes usable day to day, not just a finance policy.
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• This is why control software matters as much as the model. Running on prem means managing Kubernetes, GPUs, drivers, networking, security, quotas, and updates across many clusters, sometimes across thousands of edge locations. The bottleneck is usually operations, not whether a model can technically run on a box.
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• The competitive map is shifting toward full stack AI factory platforms. NVIDIA now frames enterprise AI as a validated factory design, and Red Hat packages OpenShift AI with NVIDIA for hybrid environments. In that world, open models are the fuel, and the winning platform is the one that makes local deployment, routing, and governance feel turnkey.
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Over time, more enterprise inference will move into company controlled environments, especially for repetitive internal tasks and edge use cases. Frontier models will remain important, but mainly as a premium lane for the hardest work. That pushes value toward the orchestration layer that decides which model runs where, at what cost, and under what policy.