$ cat ./how-it-works.md

How the AI Factory Works_

What happens between ordering NVIDIA hardware and the first model in production. Every step, one team.

Buying the GPUs is the easy part.

A SuperPOD arrives as crates: compute, fabric, storage, power, software. Turning crates into a factory researchers actually use takes months of specialized work, through code, automation, and strategic design, and it's where most AI projects lose a year. That work is Mark III's entire business.

An NVIDIA AI Factory, deployed and run end to end

End to end means physically. The racks get placed, the cables run, the fabric validated, the burn-in completed, by the same engineers who then operate the factory alongside your team.

01

Every component, built in-house

Every single piece of the factory is physically implemented by the same engineering team, the tens of thousands of cables, optics, and connections, so there is never a question of where anything is or how it fits together.

02

Kept running, past go-live

The factory stays healthy and online through a standing partnership with the operations team. Running AI infrastructure at this scale is genuinely hard; the engagement does not end at install.

03

Made usable for real work

A factory only matters if people use it: to run research, build products, and make the discoveries that transform the business. Enablement is part of the deployment, not an afterthought.

// design to first token
designphysical build-outburn-inproduction

Four roles, one cohesive unit

  1. 01

    System Engineers

    Architect, implement, and maintain the underlying IT infrastructure for the long term.

  2. 02

    MLOps Engineers

    Automate, deploy, and monitor models and services across cloud-native and legacy infrastructure alike.

  3. 03

    Developers

    Build AI, analytics, web, and simulation stacks on open source foundations, increasingly as AI agents: autonomous, tool-using systems that reason over your data, call your APIs, and automate real workflows end to end.

  4. 04

    Data Scientists

    Collaborate with your team on AI and analytics solutions, from first prototype to production models.

nodes — sshSystem Engineers
$ pdsh -w gpu-[01-03] nvidia-smi -L
runai — zshMLOps Engineers
$ runai login
agent.tsDevelopers
import OpenAI from "openai";
train.py — pythonData Scientists
$ python train.py

Education comes with the factory

AI Education Series

Virtual training sessions delivered in partnership with NVIDIA, so teams grow into the factory as it comes online.

AI Hackathons

Innovation competitions using Mark III's Hack methodology, turning your own use cases into working prototypes.

No silos, no hand-offs

One team designs, installs, automates, and operates. Nothing falls between vendors because there are no other vendors. That's why NVIDIA has exactly one AI Factory partner that deploys end to end, from physical build-out to production models.