Decentralized GPU Cloud for AI

ParalonCloud is a decentralized GPU network powered by DePIN (Decentralized Physical Infrastructure Network). Access high-performance compute resources from a global network of verified GPU providers for AI inference, model training, and machine learning workloads.

Our decentralized AI infrastructure connects developers with distributed GPUs worldwide. Run large language models through our OpenAI-compatible inference API. Rent GPU servers on-demand for AI training, fine-tuning, and high-performance computing.

The decentralized GPU provider network features real-time health monitoring, instant Docker-native deployment, and comprehensive utilization metrics. Every node is verified and monitored to ensure consistent performance for your AI training and inference workloads.

Become a decentralized compute provider and monetize your idle GPU capacity. Our simple Docker-based setup takes minutes. Join the network to earn rewards while contributing to the distributed AI infrastructure that powers next-generation applications.

ParalonCloud supports the full AI development lifecycle: from model training and fine-tuning to production inference at scale. Pre-configured environments include popular ML frameworks. Access via Jupyter Lab, SSH, or our REST API.

Our decentralized network architecture ensures high availability and competitive pricing. Pay only for GPU-minutes consumed with no long-term contracts. Transparent pricing set by individual providers creates a competitive marketplace for AI compute resources.

ParalonCloud

Decentralized GPU cloud for AI

Rent a GPU with JupyterLab on it, call open models through an OpenAI-compatible API, or render a scene, on hardware we measured before you pay for it.

Rent a GPU Get an API key
A graphics card seen from the front, its three fans

Rent it

Not a slice of a shared card. Pick a GPU, and a JupyterLab workspace is running on it about a minute later, billed by the minute until you stop it.

The same card taken apart: bracket, board, heatsink, shroud and fans in a row

Measured

Every card is benchmarked before it is listed and again after each reboot: compute, memory bandwidth, the link it sits on. The figures are on the machine's page.

A globe drawn in dots of light, with brighter points where the machines are

The network

The hardware belongs to independent providers around the world. Each machine and each provider carries a score from its finished rentals.

Live

This is the marketplace as it stands this minute, not a price sheet. Pick one and it is yours until you stop it.

See every machine

Available now

Benchmarked before listing

Products

01

GPU rental

A whole machine with JupyterLab on it, in about a minute. Pick it in the browser, or start it from code with the Rental API.

Browse GPUs
JupyterLabReady
GPU84%
VRAM18 / 24 GB
02

Inference API

Open models behind an OpenAI-compatible API, priced per token. Swap the base URL and keep your code. Claude Code works too, through the Anthropic Messages API.

Explore the API
03

Rendering

A render farm on the network's GPUs. Upload a .blend, pick Cycles or Eevee, download the frames.

Browse renderers

kitchen-scene.blend

Cycles · 1920×1080

0% rendered

The terms

Billed by the minute

No contract and no minimum. You are not billed for the minutes a machine was down on the provider's side.

No egress fees

Pull datasets and push checkpoints without a bandwidth bill on top of the GPU price.

Spot machines

Discounted GPUs the provider may take back with two minutes' notice, for work that can be resumed.

From code, too

Rent over HTTP with the Rental API, or from Claude Code, Claude Desktop and Cursor through the MCP server.

For providers

Connect a GPU server, a workstation or a Mac. It earns when someone rents it and when it serves inference, and you decide when it is available.

  • One Docker command connects the machine
  • You set the price, or take the suggested one
  • You keep 80% of every rental
  • Paid in USDC to your own wallet
Become a provider See the leaderboard

Run on the machine

docker run -d --gpus all \
  -v /var/run/docker.sock:/var/run/docker.sock \
  -v paralon-cache:/opt/paralon \
  -v /etc/machine-id:/host-machine-id:ro \
  -e TOKEN="your_token" \
  ghcr.io/paraloncloud/agent:latest

✓ Node registered and online

✓ GPU detected: RTX 4090

Sign in, add credits and start a machine. You pay for the minutes it runs and nothing else.

Open the dashboard See pricing

In partnership with

Romanian-American University