The data cannot leave. So the model comes to it.
Private AI means running open-weight models on hardware you control instead of sending prompts to a vendor API. Pexon covers the stack that decision creates: GPU sizing, model selection and quantisation, a vLLM or TensorRT-LLM serving layer, and access control bound to your existing directory. Built for industrial companies between EUR 50m and 2bn revenue.
The model is rarely the constraint.
Open-weight models have narrowed the capability gap far enough that whether they can do the work is no longer the interesting question. What is left is memory bandwidth, KV cache, quantisation and an operations story your platform team can actually carry after handover. That is engineering, and it is where these projects fail.
Common questions
When does private AI beat a hosted model API?
When the data classification forbids the API, when the workload is large and steady enough that per-token pricing stops being cheaper than owned hardware, or when a customer contract requires processing inside your own estate. Bursty, low-volume workloads are usually better served by an API, and we will say so.
Do we need to buy GPUs before we start?
No, and buying first is the common mistake. The sizing work runs on rented capacity or on whatever cards you already have, because the point is to establish how many you need. Procurement happens after the arithmetic, not before it.
Not a sales call. An architecture call.
Thirty minutes with the architect who would actually run the engagement.