The population-scale arithmetic of Green Intelligence
One AlifZetta node can serve a district. One GPU rack cannot power itself. Do the math for a country.
The one-district test
A single AlifZetta node handles thousands of queries per second against a ninety-thousand-entry NEXUS substrate at sub-five-millisecond latency. That is enough capacity to serve a district of five hundred thousand people through their citizen-service portal, in real time, with zero external cloud dependency.
Scaling to a nation
Nepal has seventy-seven districts. That is seventy-seven office-rack-sized nodes. Total power draw: under eight kilowatts. Total grid impact: negligible. GPU-based equivalent: multiple megawatts of dedicated substation capacity, most of which the country does not have. Green Intelligence is the only architecture where the math works out at country scale for the emerging world.
What we are proving in Nepal
The Nepal deployment is not a demo. It is the working proof that sovereign, cited, population-scale AI can run on the electricity budget of a small office. When that story spreads across South Asia and Africa, the frontier-lab TAM story looks very different.
Why we use different words
Where the industry says LLM, we say CLLM. Where it says vector database, we say NEXUS. Where it says Chain-of-Thought, we say PRISM proof-tree. Where it says RAG, we say LATTICE. Where it says trillion-token dataset, we say Smart Router Dataset. Different words because different architecture. Read the whitepaper.