Frontier AI,inside your 
.
We compress frontier open-weight models until they fit on hardware you already own. The weights never leave the building.
01 — The constraint
The 
.
It was never that the model was slow.
It was that it didn’t fit.
A frontier model needs more memory than any single accelerator has. So teams rent multi-GPU nodes, or settle for a worse model.
The enemy is memory. Not compute.
02 — Same model
Same model. Same 122 billion parameters. Still generating.
03 — Deployment
Four GPUs. Become one.
04 — On a laptop
This is what a 30-billion-parameter model feels like with no cloud behind it.
Screen output is an illustration, paced at the measured 12.7 tokens/sec.
05 — Inside your walls
weights leave your building.
On-prem. Air-gapped if you need it. No outbound calls. No third party holding your data. The model runs where your data already lives.
Built for
Anyone whose data can’t go to the cloud.
Banks
Client records, credit files, trading models. None of it goes to a third-party API.
Hospitals
Patient data stays on the network it was collected on.
Defense
Air-gapped by requirement. The model comes to the data.
06 — The method
Not every weight deserves the same precision.
A few weights route every token. They stay at full precision.
Almost everything else goes to the extreme.
Uniform compression treats them all the same, and breaks. Ours holds.
07 — Quality retained
median quality retained across six benchmarks.
Two of them hold less well. They’re on this page too.