Does every AI request really need a data center?
For most everyday tasks — no. On-device AI uses a fraction of the energy, keeps your data on your device, and works without a network connection. We're building the tools to make it practical.
Read the white papersThe gap is closing
Cloud AI has a meaningful head start in raw capability. That gap is narrowing fast. Today's on-device models handle a large and growing share of everyday tasks — drafting text, answering questions, summarizing documents — without touching a server.
Where precision matters most, on-device models can call reliable software libraries (numpy, scipy, and similar tools) for deterministic, verifiable results rather than probabilistic guesses from a distant data center.
When the cloud goes down, your AI shouldn't
Cloud AI is a single point of failure. One provider outage and every dependent user, app, and business stops cold — simultaneously. On-device AI has no such dependency. No network required. No provider uptime to monitor. No rate limits, surprise model changes, or cascading failures from a data center under pressure.
On-device AI also changes what happens to your data. With cloud AI, everything you type is sent to a remote server — which may include health records, legal documents, or a child's schoolwork. With on-device AI, nothing is transmitted. It's privacy you can verify rather than trust — a property of the architecture, not a promise buried in a terms-of-service.
Greener, more reliable, and more private — three arguments, one architecture. Less energy consumed. Less water used. Still working when the internet isn't. And your data never left the device.
What we're doing
EcoInference.ai is a research and engineering effort to make on-device AI practical at consumer scale. We build tools, publish findings, and make the case — with evidence — that reducing AI's environmental footprint doesn't require sacrificing usefulness. Two white papers are published so far: The Case for Greener AI examines the energy and water cost of cloud inference; AI Data Center Overbuild asks how much of the projected buildout is actually necessary — and who is paying for it.
The work is led by Mark J. Divitt, a principal engineer with two decades building large-scale cloud systems, now focused on what happens when that infrastructure is no longer necessary. More about Mark →