E-waste and lifecycle — the twenty-year tail
GPU boards are hard to recycle. Standard servers are not. The lifecycle audit is not close.
What happens when the rack retires
A five-year-old datacentre GPU is functionally obsolete for frontier workloads. It has to go somewhere. The rare-earth composite structure makes clean recycling economically unattractive — most retired GPU boards go to bulk-scrap processing, where the rare-earth content is largely lost.
The commodity-CPU alternative
A five-year-old Ryzen server can serve inference for another five years — the CPU-only architecture ages gracefully. When it does retire, the disposal chain is the same as any standard server: recover the metals, recycle the plastics, done. That is not invisible — it is engineering.
The regulatory tail
EU e-waste rules are tightening. GPU-heavy deployments are already on the enforcement radar. Green Intelligence enters that regime with a much cleaner lifecycle audit. That is a structural advantage that compounds as regulation catches up with the industry's growth.
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.