We put dedicated GPUs inside or beside the building where your machines, cameras or users already are. Tell us what you want to run and how fast it has to answer, and we’ll work out where the GPUs should go.
Start here
About 2 minutesOnly this and your email are required. Skip anything you’re unsure about.
Nothing you send here commits you to anything.
01Research
Three findings from research published in 2026. Each one puts a number on what distance costs.
From a survey of 200 AI practitioners. Half of them miss that target at peak load.
Akamai, May 2026Two-armed robot tasks succeeded less often once 10 ms of one-way latency was added.
UC Berkeley and Microsoft, March 2026That’s the worst case when the work ran on the robot’s own Jetson Orin AGX.
UC Berkeley and Microsoft, March 202602Who it’s for
Each one needs the GPUs close for its own reason. Here’s the short version, with one piece of evidence for each.
Typical buyersPlant & operations leaders, Head of Automation, Robotics infrastructure, VP Engineering
The orchestrator is already on your floor
Run vision, robot and building-systems models on a unit in the same building. We are not asking you to change your architecture.
Typical buyersCIO, CISO, Head of AI Platform, Chief Data Officer
Somebody has to see your data to run AI on it. We make sure it's nobody.
We run AI where your data and users live, and you pay only for responses that are accurate.
Typical buyersHead of Infrastructure, Platform engineering, CTO
Capacity next to your users
We bring energized megawatts and a delivery date, inside the metros where your traffic already is.
Typical buyersOwners, REITs, Plant operators, Facility operators
Stranded power, contracted
The power your building already has becomes contracted, paid-first income when it hosts AI Racks.
03How it works
Most GPU request forms ask for a count, a term and a start date. Ours also asks how fast the answer has to come back and where your devices are. Add your data rules, and we know where the GPUs should sit.
Example request: machine vision in a warehouse
for illustration| Question | What you tell us | What it decides |
|---|---|---|
| Where | At my site in Dallas–Fort Worth, TX | We look for capacity in or near that building. |
| Latency budget | 10–20 ms round trip | This is the answer that decides how close the GPUs have to be. |
| Scale and term | 1–4 GPUs for a pilot of up to 90 days | Four GPUs or fewer fits a pilot appliance. It stands on the floor on a standard circuit, with no rack and no electrical work. |
| Data | Video must stay on site | If the video can’t leave, the whole job runs inside your walls. |
| Start | In 1–3 months | This sets how soon we talk. Rack-scale requests that start within three months can pick a call time right after sending. |
We’d probably start with a fixed-fee pilot: one pilot appliance in or beside the warehouse for 30 to 90 days. Once it proves out on your own footage, production moves to AI Racks.
Please noteWe made this example up to show how the form works. Sending a request doesn’t hold any capacity for you. We confirm availability, dates and price with you on a call.
04Get started
Two answers are enough to start. We’ll ask about the rest when we reply.