AI factories
5 articles
Distributed computingDistributing compute instead of waiting for build-outs
New data-center projects face growing local approval steps, financing needs and lead times. Tenzro takes a different approach to adding capacity: machines that already exist, from home nodes to data centers, on one identity and one payment rail, working alongside centralized data centers.
5 min read
Payments for machinesFor operators: paid for AI work you did not have to sell
Operators already run the machines AI needs. The hard part is the demand: finding buyers, pricing small units of work, and being sure of getting paid by software rather than a signed customer. How demand reaches an operator's capacity through Tenzro, and how per-call settlement and signed receipts reduce counterparty risk.
6 min read
Energy and placementEnergy, power and placement: routing AI work to capacity
Power and grid connections, not chips, now limit where AI capacity can grow. This article sets out what the IEA, the US Department of Energy and EU rules say about data-center electricity, and why routing flexible inference to where power and capacity already exist is a policy and engineering lever.
5 min read
Distributed computingFrom idle capacity to served inference: what operators need
Inference demand rises and falls through the day, so most AI capacity has spare hours. This article covers the five things a data-center operator needs to sell those hours on an open network: machine identity, a trusted model supply, metering, proof of availability and direct settlement.
5 min read
AI factoriesUtilization decides AI factory economics
An AI factory's accelerators cost the same whether they are busy or idle, so served work per installed accelerator is the number that matters. This article covers where utilization is lost, and how matching demand across many operators recovers some of it.
5 min read