Why we're different
Built for the efficient edge — not the megawatt race
Most of the industry is chasing ever-larger, power-hungry facilities. We do the opposite: small, grid-light, efficient units placed exactly where the demand is.
Power
The field: grid-tied, or reliant on stranded grid power.
Green EDGE: grid-light — designed for contracted renewable supply + battery buffer.
Footprint
The field: megawatt halls chasing large-scale AI.
Green EDGE: micro modules sized for the real edge — towers, exchanges, urban sites.
Compute
The field: power-hungry racks and heavy cooling.
Green EDGE: efficiency-first compute — the same resident model capacity inside about 17× less facility power.
Workloads
The field: centralised capacity, far from users.
Green EDGE: 5G/MEC, edge-AI, smart-city and caching — next to users.
The efficiency is not primarily a saving
It is what makes the siting possible — and the siting is what
makes the capacity obtainable at all.
~1,408 kW
the same model capacity held on GPU infrastructure
1.50×
less energy per unit of memory bandwidth delivered
4 hours
contracted autonomy, sized to the full facility load
Generating tokens from a large language model is limited by memory bandwidth, not raw
arithmetic — the weights must be read for every token produced. Unified-memory silicon holds the model
and reads it fast for very little power. Holding the same capacity in high-bandwidth memory on conventional
GPU infrastructure needs roughly 1,408 kW of facility power: a data hall, three-phase supply,
mechanical cooling and a grid connection quoted in years. Ours is a 52 kW load — small enough that a
new grid connection is not the starting point for a deployment. That is why we can put capacity
where conventional infrastructure cannot go — and why a competitor holding a place in a
connection queue cannot shorten it.
Comparator: published NVIDIA DGX H100 specification at a typical enterprise PUE, against Apple’s
published measured maximum of 385 W per M5 Ultra node. Both figures are published, and Apple’s is a
stress test, so the comparison is conservative. The model capacity compared is what one module holds at
512 GB per node.
We make no claim about the cost of a token — that is set by the model providers.
