The April Build
Not for a client. Not for money. To understand what enterprise AI actually feels like from the inside.
How it happened
Sunday, April 13 · 1:00 PM
Laid off
Laid off from a 4.5-year role. Solar industry policy changes hit the company hard. It stung — but I'd been ready for a change. I'd grown the business significantly, built things that made real money, and never felt like any of it was truly valued.
Sunday, April 13 · 8:20 PM
NVIDIA says yes
Seven hours after getting laid off, I got an email from NVIDIA telling me I'd been accepted into their Connect program. I took that as a sign and kept moving.
Friday, April 17 · The build begins
Started building
Started building Friday night. 26 hours of building ahead. NVIDIA NIM. Docker. Cloudflare Zero Trust. AnythingLLM. Grafana. All of it from scratch on a GH200 superchip.
Monday Morning, April 20 · Done
Full stack deployed
Full stack deployed — 26 hours later. A working private AI gateway running Llama 3.3 70B. The kind of infrastructure companies pay enterprise money for. Built to understand it from the inside.
Friday, April 25 · Pivot
Stop selling. Start sharing.
After sending a blast to 5,500 Chamber contacts and getting one reply, the message was clear. Small businesses aren't ready yet. The real opportunity is inside companies where trust already exists and someone can actually move things forward.
What I actually built
A full private AI gateway — the kind of system companies pay $50,000+ to stand up. Running on NVIDIA GH200 hardware. Open source tools. Built by one person in 26 hours.
NVIDIA NIM
Running Llama 3.3 70B — enterprise-grade inference on GH200 hardware
AnythingLLM
Frontend interface for the private AI gateway — clean, functional, deployable
Docker
Containerized the entire stack for portability and reproducibility
Cloudflare Zero Trust
Secure access layer — no VPN, no exposed ports, enterprise-grade security
Grafana + Prometheus
Real-time monitoring and observability for the full infrastructure
NVIDIA GH200
96GB HBM3e, 72 Arm cores, 900 GB/s bandwidth — the superchip underneath it all
What I learned
The companies that own their own AI infrastructure aren't just saving money on API calls. They're building a competitive advantage that compounds over time. Data sovereignty matters.
Most businesses are using ChatGPT through a browser. The distance between that and a private inference stack is enormous — and most people don't know what they're missing.
I'm not a DevOps engineer. I figured this out by being willing to be confused, ask questions, read documentation, and try things until they worked. That's the actual skill.
No amount of reading about AI infrastructure prepares you for actually running it. The latency, the resource management, the failure modes — you only learn these by doing.
"I built it not to sell it but to understand it. It changed how I think about everything."— Matt Gamble, April 2026