Carbon per query — the number that matters more than parameter count
Every AI query has a carbon cost. Ours is measurable, small, and improving. The industry's is opaque and growing.
The metric that should be on every model card
Nobody publishes carbon-per-query. Instead we get parameter counts and benchmark numbers. The parameter count you should actually care about is grams of CO₂-equivalent per one thousand answered queries. For a frontier LLM served from a GPU cluster in a coal-heavy grid, that number is somewhere between 20 and 200 grams. For AlifZetta on commodity CPU under a hydro-dominant grid, it is under 2 grams.
Why the delta compounds at scale
Multiply by ten million queries a day. Multiply by three years. The difference is a small coal-fired power plant. That is what one national-scale AI deployment costs the climate under GPU-first architecture. Under Green Intelligence, the same deployment costs a rounding error.
What we commit to publish
AlifZetta will publish carbon-per-query in the next whitepaper revision, along with our methodology, our grid-mix assumptions, and our audit trail. If the industry follows suit, the buyer conversation on sustainability will finally have honest data.
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.