Resource network
GPU capacity for training, rendering and serving — drawn from the machines in your gram, or from hardware stood up inside your own walls.
Metered in VRAM-hours
The same resource, the same meter, three answers to “whose machine is this?” You choose per workload, and you can move a workload inward when the rules tighten.
On your device
Available
Inside your gram
Available
On a stranger’s machine — not available
Available
Each node benchmarks itself and signs the result with its own key. Where a benchmark does not exist yet, the node says so and emits nothing in its place. So do we.
Signed metric
gpu.throughput
Unit
name · VRAM · utilisation
The node reports its GPU inventory — model, total VRAM, current utilisation — read from the vendor tooling on the machine, and signs it.
This is an inventory, not a benchmark. A signed GPU throughput number is configured and not yet benchmarked, and the node emits no fake one in its place. Detection currently depends on NVIDIA tooling. When we can measure it, it will appear here signed, and not before.
The industry sells GPUs by model name and lets you assume the performance. We would rather hand you an inventory we can sign and tell you plainly which benchmark is still missing.
Free on your own hardware. The meter only runs when you consume someone else’s.
There is no 24/7 operations centre, because we do not employ one. Instead a node that cannot prove it is healthy is evicted from the ring rather than quietly serving your work. Fail-closed, not fail-silent.
Bring hardware onto your own site, funded upfront or financed. It is yours; we operate it.
Every resource above reaches your workload through the same substrate, whichever ring it was drawn from.
Each node runs k3s. One orchestration layer schedules all seven resources, so a workload moves between rings without being rewritten.
Workloads that cannot be containerised run as virtual machines on the same cluster, through the open-source KubeVirt project. Same scheduler, same meter.
Standard containers, standard VMs, content-addressed objects, exportable receipts. The cost of leaving is the reason to trust the platform.
GPU is metered in VRAM-hours. Each unit of work produces a receipt naming the node that performed it and the price it was charged at, on the one ledger the whole platform shares. We publish no hourly rate table on this page, because a price is meaningless without the signed unit it is counting.
See how we price compute →Not a specification, not a vendor sheet. The best verified result any node on this network has signed for gpu — and, below it, everything else that node put inside the same signature.
gpu.throughput
67.23 TFLOP/s
A benchmark reports one number and signs several. These travelled inside the same signature, so they are as verifiable as the figure above — and they are usually the ones that decide whether the machine suits your work.
dtype
fp16
dtype
Signed by the node. We have not written an explanation for this field yet, and would rather show it unexplained than invent one.
elapsed_ms
102
elapsed_ms
Signed by the node. We have not written an explanation for this field yet, and would rather show it unexplained than invent one.
iterations
50
iterations
Signed by the node. We have not written an explanation for this field yet, and would rather show it unexplained than invent one.
matrix
4,096
matrix
Signed by the node. We have not written an explanation for this field yet, and would rather show it unexplained than invent one.
mem_total_mb
12,288
mem_total_mb
Signed by the node. We have not written an explanation for this field yet, and would rather show it unexplained than invent one.
Method
torch fp16 matmul on the card, 5 warm-up + timed iterations, 2·n³ FLOP each; pod on a time-sliced slot
method
What was actually run, in the words of the node that ran it.
name
NVIDIA GeForce RTX 3080 Ti
name
Signed by the node. We have not written an explanation for this field yet, and would rather show it unexplained than invent one.
source
epn-gpu-bench job (docker.io/pytorch/pytorch:2.5.1-cuda12.4-cudnn9-runtime)
source
Signed by the node. We have not written an explanation for this field yet, and would rather show it unexplained than invent one.
gpu.throughputrecorded 2026-07-09GPU detection reports an inventory — model, VRAM, utilisation — and no throughput benchmark. A signed throughput benchmark is configured but not implemented.