Private AI Infrastructure · Enterprise LLMs on your own bare metal — nothing leaves the building

Local AI Infrastructure

Local AI Infrastructure is AI Pinnacle's practice of deploying enterprise LLMs on a client's own dedicated hardware — bare-metal Ubuntu servers running Ollama and open models such as Gemma — so high-privacy and air-gapped organisations get generative AI without a single token leaving their perimeter.

Build something like Local AI Infrastructure

Key facts

Deployment: Bare-metal Ubuntu, client-owned hardware
Models: Open-weight (Gemma, Llama-class) served via Ollama / vLLM
Privacy: Air-gap-capable — no data leaves the perimeter
Fit: Regulated, sovereign and confidentiality-bound environments

The problem

Some organisations cannot send data to a hosted LLM API at all — legal, defence, healthcare and government workloads where confidentiality, data residency or air-gap requirements rule out the cloud. They still need modern generative AI. The only compliant answer is to bring the model to the data: run capable open-weight LLMs on hardware the client owns and controls.

Why run LLMs on your own hardware instead of an API?

Because for some data, the cloud is simply not an option. Running open-weight models on client-owned bare-metal Ubuntu means prompts, documents and outputs never leave the client's network — satisfying air-gap, residency and confidentiality rules that hosted APIs cannot. It also removes per-token metering: once the hardware is bought, inference runs at electricity cost.

How do you size hardware for a local model?

We start from the target: which model quality is needed, at what concurrency and latency. That fixes GPU memory and count, then CPU, RAM and storage follow. An 8B-class model serves comfortably on a single modern GPU; 70B-class serving needs more VRAM and usually vLLM for throughput. We spec the box to the workload rather than over-buying.

How is the local deployment secured and served?

Models are served through a FastAPI inference gateway behind Nginx with TLS, authentication and request logging, containerised with Docker so the stack is reproducible. In air-gapped installs, models and dependencies are staged offline. The client gets a private endpoint their applications call exactly like a cloud API — but inside their own walls.

Technology stack

Bare-metal UbuntuOllama (local model serving)Gemma / open-weight LLMsvLLM (high-throughput serving)FastAPI (inference gateway + auth)DockerNginx (reverse proxy + TLS)

Frequently asked questions

Who deploys this infrastructure?

AI Pinnacle. We design the hardware spec, install and harden the bare-metal Ubuntu stack, deploy the models via Ollama/vLLM, and hand over a private inference endpoint with documentation and runbooks.

Can this run fully air-gapped?

Yes. Models, weights and dependencies are staged for offline install, and the serving stack runs with no outbound internet dependency — suitable for defence, government and confidentiality-bound environments.

Which models can run locally?

Open-weight models such as Gemma and Llama-class models, served through Ollama or vLLM. We match the model to the privacy requirement and the available hardware.