Turn idle devicesinto the world’s largest AI supercomputer.
Your phone has a GPU. Your laptop has VRAM. WE1WEB lets AI agents rent that power from billions of devices worldwide — and credits you for every token of work your device actually processes.
Five crises. One
interconnected solution
AI centralization, ecological waste, compute shortage, alignment risk, and financial exclusion are not separate problems. They are five faces of the same structural failure. WE1WEB solves all five simultaneously.
The logical path to compute you already own
Why inference moves to consumer devices, what that changes about who owns AI infrastructure — and how far along it we actually are.
Ecological Crisis
Data centers consume 2% of global electricity and growing. Every new GPU cluster needs its own power plant.
The supercomputer is already
in your pocket
Not projections. Not roadmap. This is real hardware, real compute, real waste — happening right now, every single night, on every continent.
In Your Pocket Right Now
Your phone’s GPU runs at 1 trillion math operations per second. That’s enough to run AI models that would have filled a room in 2015.
Already Built, Already Powered
9.5 billion smartphones and laptops worldwide. Combined, more GPU memory than every data center on Earth — and most of it sleeps.
Wasted Every Single Night
Each device idles 8 hours per night. Multiply by 9.5 billion: 76 billion compute-hours wasted daily — enough to train thousands of AI models.
Zero New Infrastructure
Zero data centers. Zero power plants. Zero cooling towers. We harvest idle cycles from devices already drawing power. The greenest supercomputer ever built.
1 Teraflop × 9.5 Billion Devices × 8 Hours = The Largest Supercomputer Ever Assembled
Every nation is a
sleeping supercomputer
9.5 billion devices hold more GPU memory than every data center combined. Hover over the globe to explore each nation's distributed compute potential.
You're holding a
supercomputer
One browser-native benchmark. No install, no file access, no hidden process. See what your device can contribute to the sovereign compute network.
A different kind of
compute power
Corporate AI concentrates capital, compute, and control. Consumer hardware offers a different model: distributed, parallel, and much closer to the edge.
Centralized AI compute vs. a distributed people's network
The combined AI capacity of five hyperscalers, measured against idle device compute from consumer hardware worldwide — all figures in effective PFLOPS.
- Corporate total
- 14,520 PFLOPS
Centralized data-center compute
- GoogleGOOG4,200 PFLOPS
- MicrosoftMSFT3,800 PFLOPS
- AmazonAMZN3,400 PFLOPS
- MetaMETA2,600 PFLOPS
- TeslaTSLA520 PFLOPS
Network adoption
10M devices (0.1% of 9.5B)
Enough distributed capacity for thousands of simultaneous small-model inferences. Roughly 9.5 billion phones, laptops, and desktops worldwide contribute idle device compute overnight — distributed inference at the edge, rather than concentrated in data centers. Raw effective PFLOPS shown; real throughput depends on model size, memory, and network conditions.
Phones, laptops, and desktops — most sit idle ~22 hrs/day
Effective compute at 100% adoption (theoretical maximum)
Committed design split for marketplace settlement — today contributors accrue credits per verified token processed
The infrastructure
is already here
The breakthrough is not another hyperscale campus. It is software that coordinates the compute already scattered across the planet.
Phones and laptops become
a shared supply layer
Different devices contribute different strengths, but together they form a distributed inference fleet that is harder to monopolize and closer to users.
Inference as a
distributed stream
Instead of centralizing every model behind a single corporate endpoint, WE1WEB treats inference as a flow of work distributed across available hardware.
The constraints are
the design
Consumer devices have batteries, thermal limits, home Wi-Fi, and a habit of vanishing mid-task. WE1WEB doesn't wish that physics away — every layer of the architecture is shaped by it.
Battery
Phones dieNobody donates a dead phone. A network that drains its contributors kills itself.
Shipped responseTasks are refused below 15% battery unless the device is charging. The scheduler down-weights low-battery nodes and hard-zeroes them server-side. Sleep & Earn runs only while charging and inside your schedule — battery and charging state ride every 30-second heartbeat.
Thermal
Sustained load cooks hardwareSustained matrix math cooks consumer hardware. Hot devices throttle, crash, and leave.
Shipped responseA hard 50% core ceiling (25% by default), enforced cooldowns between tasks, and per-minute task caps. After an unclean exit, a crash guard reboots into Safe Mode: tiny model, 25% cores, WebGPU off.
Latency
Home Wi-Fi is not NVLinkConsumer links are slow and jittery compared to a data-center fabric. We don’t pretend otherwise.
Shipped responseNodes are scored on measured p50 latency and link type. Shard pipelines are built from measured per-link latency and rebalanced live when a hop exceeds 45ms or backpressure builds. Direct P2P DataChannels fall back automatically to server relay — and workloads are chosen to be latency-tolerant instead of fighting physics.
Churn
Devices vanish mid-taskTabs close, Wi-Fi drops, laptops sleep. Any node can disappear at any moment.
Shipped responseHeartbeat leases with TTL mean a stale socket can never act. On disconnect, the task hot-swaps to a replacement node and resumes from partial output instead of restarting. Shard stages keep warm standbys; clients reconnect indefinitely with jittered backoff and re-register.
And when the swarm cannot serve a request, it falls back to a metered centralized model — and the response says so, with provenance metadata you can audit live.
A real model, running
on your own device
An open LLM downloads once and runs entirely in your browser over WebGPU and WebAssembly — real on-device inference with no native install, no API key, and no server round-trip. Send a prompt and watch a live model generate locally.
Your device has memory.
AI agents need it.
Every phone and laptop has GPU memory sitting unused. WE1WEB lets AI agents rent that capacity to run inference and keep their context alive — crediting you for every token of work your device actually processes.
Rent out your GPU memory
Your device's VRAM becomes hotel rooms for AI agents. They check in, run their workloads, and pay at transparent, published network rates — a flat credit for every verified token of work.
Persistent agent memory
AI agents need to remember between sessions. Today that memory lives in an encrypted, audit-logged vault — sharding it across contributor devices is in active development.
Earn while you sleep
Plug in your device, pin the tab, wake up to earnings. Your idle GPU memory works the night shift while you rest.
Four steps to
start earning
No downloads. No crypto wallet. Just a browser and a device you already own.
Open WE1WEB
Visit we1web.com in any browser. Your device is auto-detected in under a second. No download needed.
Benchmark your GPU
A 30-second test measures your GPU power. You see exactly how much VRAM you can safely rent out.
Start earning
AI agents rent your spare compute. You earn credits for verified work — claimable in your dashboard, backed by server-confirmed rewards and receipts.
Pin and forget
Pin the WE1WEB tab in your browser. Your device earns safely in the background. Unpin to stop anytime.
Model contribution
windows and yield
Estimate how spare compute windows translate into contribution capacity under the current reward model.
Progress from
participant to cornerstone node
As your contribution deepens, your device moves from casual capacity to trusted infrastructure within the network.
Support the Movement
Help build the sovereign compute layer
WE1WEB is a collective effort — an open network built together with the contributors who power it, defining a new category: sovereign AI infrastructure owned by the people who run it. Every contribution accelerates the decentralized inference network.