GPU compute for teams, without owning hardware
Contributor AI gives you a pool of distributed GPUs for inference, fine‑tuning and batch jobs. You focus on models and data — we handle capacity and contributors.
How to start as a client
- 1. Create a client account. Use your work email so we can verify your company if needed.
- 2. Create your first job. Describe the workload, upload config or image, set priority and budget cap.
- 3. Watch the job run. We schedule it across suitable contributors and show live status in the dashboard.
- 4. Download results and logs. When done, you can fetch outputs via UI or API and review logs.
- 5. Scale out. Add more jobs or integrate our API into your pipelines and CI.
Pricing model
- • You are billed per GPU‑hour, plus storage and network where applicable.
- • You can set a hard budget per job; tasks stop when the limit is reached.
- • Higher priority queues reserve more capacity during busy periods.
- • Volume discounts and dedicated capacity are available for long‑term usage.
Prices aligned with Vast.ai, RunPod, Lambda Labs — competitive GPU cloud rates.
GPU pricing (per hour, USD)
Competitive with Vast.ai, RunPod and other GPU marketplaces.
| GPU | VRAM | $/hr |
|---|---|---|
| RTX 3060 | 12 GB | $0.22 |
| RTX 3080 | 10 GB | $0.38 |
| RTX 4090 | 24 GB | $0.55 |
| A100 | 40 GB | $0.58 |
Who is this for?
- • Product teams running LLM and vision inference in production.
- • Data science groups with spiky training workloads.
- • Agencies and studios that occasionally need a lot of GPU power.