Private AI without the power bill

Modern desktop-class silicon runs AI models with hundreds of billions of parameters on a box that sits on a shelf. That changes the energy math of private AI, and we build on it.

Want to reduce the need for hyperscale data centers?

Run your AI local.

Private AI will not settle the data-center debate.

It just removes your organization from it.

What a gigabyte of AI memory costs in watts

The honest way to compare machines of different classes is to normalize by the thing that determines which models you can run: memory. Divide each machine’s maximum power draw by the memory available to host a model, all straight from the manufacturers’ specification sheets, and the picture is clear.

Studio-class node (what we deploy) · 512 GB · 270 W max0.5 W per GB
Desktop AI box (closest same-class peer) · 128 GB · 240 W1.9 W per GB
Hyperscale AI server (eight accelerators) · 1,440 GB · ~14,300 W9.9 W per GB

Against its closest same-class peer, the studio-class node hosts four times the model memory at comparable wall power, roughly three and a half times more memory per watt. Against the hyperscale server, shown for scale, the gap is nearly twenty to one per gigabyte, and about fifty to one per device. This measures the power cost of hosting a model, not raw throughput; a data-center server is faster, but for a team’s private inference, capacity on a right-sized box is what the job actually requires.

Rows of high-density server racks glow inside a large data center.

The hyperscale answer: racks of multi-kilowatt servers, and a building engineered to feed and cool them.

The energy-efficient private AI option

Eco-conscious has been one of our founding commitments: we specify hardware for efficiency as deliberately as we specify it for speed. The new silicon generation makes that commitment a product option.

  • Standalone, studio-class hardware sized to your actual workload, in our FedRAMP-certified US data center or on your premises
  • Runs the same private AI workspaces, agents, and governance stack as every Modular deployment
  • Draws hundreds of watts, not tens of thousands, which shows up in your energy footprint and our fixed monthly pricing
  • Scales by adding boxes when you grow, never by renting a hyperscaler’s rack

Size it for your team

Tell us your workload and compliance picture in a free consultation and we will spec the right-sized option, with the energy numbers in writing.

Sources: studio-class maximum wall power, manufacturer power specifications and August 2026 silicon announcement; desktop AI box specifications, manufacturer product page; eight-accelerator AI server maximum draw, vendor datasheet.