GPUs online
— peak · — average · last 24 hours
- Community
- Compute fleet
History starts with the first recorded snapshot. Machines appear here once they connect.
Register your GPU and commit it to a coin. Paid jobs open with the contributor pilot; until then jobs run on managed GPUs.
Connected hardware. Available capacity. Work actually completed.
Hardware reports are unverified. Connected memory is capacity, not measured compute speed.
Compute fleet counts the GPUs Compute rents from Vast that are up now. The community pool counts the Runpod GPUs that run every coin’s images and are up now; coin GPU budgets pay for them, and any community can fund more. Neither is self-reported. Community machine reports count matching inventory received within 90 seconds. Paused, expired, revoked, and disconnected nodes do not contribute to the totals. Reports confirm a connection; hardware qualification and unique-device verification are separate.
Compute capacity counts recently connected, operator-approved GPUs with matching runtime and model qualification, available job capacity, and enabled dispatch. The pilot qualifies one GPU per node. Throughput needs a workload benchmark; memory and GPU counts cannot establish TFLOPS.
Completed work counts managed and contributor jobs with an accepted result, stored output, and settled charge. Contributor acceptance also records unpaid USD compensation. Preview jobs are excluded. Jobs in progress include dispatch and result validation; this is not a GPU utilization measurement.
Updates refresh every 15 seconds while this section is visible. An unsuccessful refresh retains the previous observation and marks it out of date. Individual machines appear in the history below only under an anonymous alias; no owner, wallet, machine name, address, or private job details are published.
Reading history…
— peak · — average · last 24 hours
History starts with the first recorded snapshot. Machines appear here once they connect.
— peak · last 24 hours
History starts with the first recorded snapshot. Machines appear here once they connect.
— peak · managed and contributor
History starts with the first recorded snapshot. Machines appear here once they connect.
Reading machines…
Machines are listed by an anonymous alias: no owner, wallet, name or address. Uptime counts the 5-minute windows recorded since a machine was first seen. Community counts the owner-reported machines, which are unverified, plus the shared Runpod pool that coin GPU budgets pay for. History is kept for 30 days.
Register machines and run the connection test. Community GPUs can’t earn yet. The current pilot supports Linux machines with NVIDIA GPUs.
For ex-mining rigs and single cards alike. Run the worker’s local check, paste what it prints, and see which work fits and what blocks the rig today.
Download the worker to the Linux machine, review it, then run:
python3 compute-worker.py diagnose
It reads nvidia-smi, makes no network request and needs no credential.
Read in this browser only. Nothing you paste is uploaded or saved.
The worker applies one fixed table per group of identical GPUs. Memory is per GPU, because it does not pool across cards. These are rules of thumb, not qualification: an operator still reviews each machine for a specific workload.
| Use | Good | Limited | Not suitable |
|---|---|---|---|
| AI inference | 24 GB+ | 12–23 GB: smaller or quantized models | Under 12 GB |
| AI training | 24 GB+ on x8 or wider links | 12–23 GB, or narrower links | Under 12 GB |
| 3D rendering | 8 GB+; risers are fine | 4–7 GB | Under 4 GB |
| Scientific and engineering | Compute capability 7.0+ and 8 GB+ | Anything else | — |