Network status

Live view of the compute pool. Updated every request; nothing here names a person or a machine.

JSON
Nodes online
5
5 contributed in the last 30 days · 2 of 2 contributors online
GPUs online
4
177 GB of GPU memory · 68 CPU threads
Average load
2%
GPU utilisation across online GPUs · CPU 2%
GPU memory in use
20%
36 of 177 GB — resident models count
Worker slots busy
0%
share of the pool’s job capacity in use right now
Average node uptime
3.5 d
since each online node last started

Work done

minutes of work per day (jobs + LLM serving), last 14 days
Last 24 hours
0 tasks
0 jobs · 0 min of task time
Last 7 days
551 tasks
330 jobs · 236 min · models hosted 751 h
LLM gateway
1 requests, 24 h
100% served · avg 987 ms · 2,527 req / 42.4M tok this week
2026-09-29: 0.0 minutes of work2026-09-30: 0.0 minutes of work2026-10-01: 0.0 minutes of work2026-10-02: 0.0 minutes of work2026-10-03: 0.0 minutes of work2026-10-04: 0.0 minutes of work2026-10-05: 2.9 minutes of work2026-10-06: 22.7 minutes of work2026-10-07: 47.6 minutes of work2026-10-08: 192.5 minutes of work2026-10-09: 190.2 minutes of work2026-10-10: 92.6 minutes of work2026-10-11: 0.3 minutes of work2026-10-12: 0.0 minutes of work09-2910-12

Contributor leaderboard

last 7 days · by work delivered
#ContributorPointsShareClipsGenerationsLLM requestsHostingNodes onlineUptime
🥇contributor-268bcc 1,237.495.7% 85157 2,321 · 42.4M tok 750.9 h 4 / 3 GPU3.9 d
🥈contributor-7c8a2f 55.44.3% 309— — — 1 / 1 GPU1.9 d

How points work. Points are minutes of work delivered to customers: one point per minute of completed task time (clips, generations, articles) and one per minute spent answering LLM requests. Keeping a model resident is availability rather than work, so it earns one point per hour — it counts, but it cannot outrank a node that is actually running jobs.

Contributors appear under a stable pseudonym, never a name, email or machine name. If you run a node, your handle is shown on your dashboard so you can find yourself here.

Generated Mon, 12 Oct 2026 12:54:50 GMT. “Average load” is GPU utilisation as reported by each node; a resident model holds memory without necessarily loading the GPU, which is why memory-in-use and load can differ. Rented burst capacity and retired machines are not counted as nodes.