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The Build

AMG AI Solutions deployed a full private AI stack on an NVIDIA GH200. Here’s exactly how it was done.

The Timeline

From first instance to full stack in a weekend

April 13, 2026 · 8:20 PM

NVIDIA said yes.

Email from NVIDIA. Accepted into the Connect Program. A few days of research, a clear direction, and by April 17th — we started building.

April 17, 2026 · Friday evening

The build begins

AMG AI Solutions spun up the first instance on the GH200 — a curiosity-driven project to understand what enterprise AI infrastructure actually feels like from the inside.

April 20, 2026 · Monday morning

Full stack deployed

Full stack deployed. 26 hours of building — and a lot of learning in between.

April 25, 2026

Knowledge becomes the product.

The infrastructure was running. The real value was what we learned building it — and how that knowledge now shapes every client engagement.

What We Built

A full private AI gateway — the kind of system companies pay enterprise-level fees to stand up. Running on NVIDIA GH200 hardware. Open source tools. Deployed in 26 hours.

Every employee gets their own private AI workspace. Documents stay on your server. Conversations never leave your network. No data going to OpenAI, no shadow AI leaking through the cracks. Full visibility through Grafana — GPU temperature, request volume, latency, everything live.

NVIDIA GH200 SuperchipHardware backbone
NVIDIA NIMInference engine — Llama 3.3 70B
AnythingLLMMulti-tenant frontend
Docker / docker-composeContainerized deployment
Cloudflare Zero TrustNo public IP, all traffic secured
Grafana + Prometheus + DCGMLive monitoring dashboard
Linux (Ubuntu)Server operating system

What We Learned

The technology is actually ready.

Most organizations aren’t — and that’s a people and process problem, not a tech problem.

Shadow AI is real and it’s already in your building.

Your staff is using ChatGPT, Claude, Gemini, whatever — right now, with your company data. Most leadership has no idea how much.

Private AI infrastructure is more accessible than anyone is selling it to you as.

We built enterprise-grade infrastructure in 26 hours. The hard part isn’t the build.

The hard part is trust and adoption.

Getting teams to change how they work — that’s the real challenge. And that’s where we focus.

Why We Built It

Not to get rich. Not to build a product. To understand it from the inside.

We’d spent years watching AI tools get sold to companies that weren’t ready for them. We wanted to know what “ready” actually meant — what it takes to run this infrastructure, what it costs, what breaks, what works.

Now we know. And that knowledge is what AMG AI Solutions brings to every client engagement.

Let’s talk →