Deployable next to the desk that runs it
A sovereign SI node you can put in a broom cupboard is a very different product from one you cannot.
The physical footprint most vendors miss
A single-unit AlifZetta server fits in a standard 1U rack. It runs at office ambient temperature. It makes no more noise than a workstation. You can deploy it in a hospital corridor, a ministry server room, or a bank branch — none of which can host a GPU rack without renovation.
Why the cupboard test matters
Every enterprise sales cycle for GPU AI includes a datacentre readiness review. Power feed, cooling capacity, floor loading, fire suppression. That is months of quotation and construction before the AI can even start serving users. AlifZetta ships past that entire gate — because it does not need it.
The reach implication
Every hospital ward, every ministry district office, every rural bank branch is a potential deployment site. That is a market topology GPU-first vendors cannot address. Green Intelligence is how you get there.
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.