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OpenGPU Network

On-demand GPU compute for AI.

Demand

  • LLM inferenceRelay
  • Fine-tuningClient
  • 3D renderingClient
  • Image & videoRelay

Routing layer

Supply

  • H100datacenter
  • A100cloud
  • RTX 4090home rig
  • Apple M-seriesedge

Run AI inference, training and rendering on a global network of GPUs, at up to 70% lower cost than traditional cloud.

325+

Active GPU providers

40+

Locations worldwide

<2s

Average routing time

98.2%

Task success rate

Why OpenGPU

Idle GPUs, put to work.

Most of the world's GPUs sit idle while AI teams wait for capacity. OpenGPU connects the two in one open marketplace for GPU compute.

Ride-sharing

Idle cars to rides

Drivers earn from cars they already own. Riders get a car in minutes.

Home-sharing

Spare rooms to stays

Hosts earn from space they already have. Guests get more choice for less.

OpenGPU

Idle GPUs to compute

Providers earn from GPUs they already run. AI teams get capacity on demand at up to 70% lower cost.

Need GPUs

Rent GPU compute without the cloud premium.

Call AI models, run jobs or rent GPU servers from a global pool. No queues, no idle hours.

  • Per token, per task or per hour, your choice
  • Automatic failover if a node drops mid-job
  • Card or invoice billing through Relay
  • Up to 70% cheaper than major clouds

Have GPUs

Earn money with your GPU.

From a single gaming PC to a datacenter fleet. Install the Provider Suite and the network routes paid AI work to your GPUs.

  • Earn for every completed task
  • No upfront commitment to start
  • macOS, Windows and Linux (Docker)
  • Track earnings and health in one dashboard

The easiest way
to use OpenGPU.

One API for 80+ models, GPU instances by the hour and a no-code studio. Pay by card or invoice.

Where requests run

Open-weight models on network GPUs, priced in real time by providers.

from openai import OpenAI

client = OpenAI(
    base_url="https://relaygpu.com/v1",
    api_key="relay_sk_...",
)

resp = client.chat.completions.create(
    model="deepseek/deepseek-v3.1",
    messages=[{"role": "user",
               "content": "Hello, OpenGPU"}],
)
# OpenGPU Mode: served from the OpenGPU Network
print(resp.choices[0].message.content)
import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://relaygpu.com/v1",
  apiKey: process.env.RELAY_API_KEY,
});

const resp = await client.chat.completions.create({
  model: "deepseek/deepseek-v3.1",
  messages: [{ role: "user", content: "Hello, OpenGPU" }],
});
console.log(resp.choices[0].message.content);
curl https://relaygpu.com/v1/chat/completions \
  -H "Authorization: Bearer $RELAY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek/deepseek-v3.1",
    "messages": [{"role":"user","content":"Hello, OpenGPU"}]
  }'

200 OKResponseOpenGPU Network

Hello! This answer was generated on GPUs in the OpenGPU Network, priced in real time by the providers who ran it.

80+

Models, one key

100%

OpenAI SDK compatible

$10

Free credit to start

RelayStudio· Make AI videos, images and voice-overs, no code needed
  • Chat
  • Reasoning
  • Code
  • Image generation
  • Video generation
  • Voice-overs
  • Open-weight models
  • Frontier models
  • OpenAI SDK compatible
  • One API key
  • 80+ models
  • GPU instances by the hour
  • No-code Studio
  • Card or invoice billing
  • Real-time provider pricing
  • $10 free credit

How it works

How GPU workloads run on OpenGPU.

  1. 01 · Client

    Submit

    Send a job through Relay or the Client App with GPU type, VRAM, budget and runtime limits.

  2. 02 · Network

    Route

    The routing layer picks the best GPU right now, using hardware, health, latency, price and reputation.

  3. 03 · Provider

    Execute

    The job runs in an isolated container. If a node drops, it resumes elsewhere from the last checkpoint.

  4. 04 · Protocol

    Verify

    Results are checked before acceptance. Critical tasks can be validated by multiple providers.

  5. 05 · Network

    Settle

    The client pays for the completed task, the provider is paid, and the full record appears in the Management App.

GPU capacity

H100, A100, RTX 4090 and more.

Datacenter

NVIDIA H100

80 GB HBM

Large-model training and high-throughput inference.

Datacenter

NVIDIA A100

40 / 80 GB HBM

Fine-tuning, distributed jobs and batch inference.

Prosumer

RTX 4090

24 GB GDDR6X

Image and video generation, rendering, mid-size LLMs.

Edge

Apple M-series

Unified memory

Lighter inference and on-device-class workloads.

GPUs, CPUs and specialized AI accelerators from independent providers, datacenters and cloud overflow, blended into one network.

  • GPUsConsumer to datacenter class
  • CPUsCPU-only nodes for lighter and pre/post-processing jobs
  • AI acceleratorsPurpose-built silicon for inference and training
  • Edge devicesCompact hardware for lightweight inference
  • Independent providersCost-efficient GPU nodes from independent operators worldwide.
  • DatacentersEnterprise-grade clusters for stable, predictable performance.
  • Enterprise & cloud overflowReserved nodes and cloud GPUs for rare hardware or demand spikes.

Enterprise

Enterprise GPU capacity, billed for use.

Cloud GPUs bill by the hour, idle or not, with a margin on top. OpenGPU bills for the work done and pays providers directly. That is where the savings come from.

Traditional cloud100%
ComputeMarginIdle time
OpenGPUup to 70% less
Compute
Illustrative. Savings vary by GPU type, region and workload.

Get started

Start using GPU compute today.

Rent GPUs for your AI workloads, or earn by sharing yours.

What do you want to do?