Ride-sharing
Idle cars to rides
Drivers earn from cars they already own. Riders get a car in minutes.
OpenGPU Network
Demand
Routing layer
Supply
Run AI inference, training and rendering on a global network of GPUs, at up to 70% lower cost than traditional cloud.
Live network data.
See it in the Management App325+
Active GPU providers
40+
Locations worldwide
<2s
Average routing time
98.2%
Task success rate
Why OpenGPU
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.

Ways to use it

CreateAI video, image and voice with no code.
Per credit
Call80+ models behind one OpenAI-compatible endpoint.
Per token
RunYour containers as tasks, routed to the right hardware.
Per task
RentFull machines with SSH, storage and networking.
Per hour
ReserveReserved pools and clusters with SLAs.
ContractFully managedFull control
From AI models via API to rented GPU servers and dedicated clusters. Choose how much control you need.
Need GPUs
Call AI models, run jobs or rent GPU servers from a global pool. No queues, no idle hours.
Have GPUs
From a single gaming PC to a datacenter fleet. Install the Provider Suite and the network routes paid AI work to your GPUs.
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.Trusted hosting partners for the lowest latency on frontier models.
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# Direct Mode: served by a trusted hosting partner
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 NetworkHosting partner
Hello! This answer was generated on GPUs in the OpenGPU Network, priced in real time by the providers who ran it.Hello! This answer came from a trusted hosting partner, for the lowest latency on frontier models.
80+
Models, one key
100%
OpenAI SDK compatible
$10
Free credit to start
How it works
01 · Client
Send a job through Relay or the Client App with GPU type, VRAM, budget and runtime limits.
02 · Network
The routing layer picks the best GPU right now, using hardware, health, latency, price and reputation.
03 · Provider
The job runs in an isolated container. If a node drops, it resumes elsewhere from the last checkpoint.
04 · Protocol
Results are checked before acceptance. Critical tasks can be validated by multiple providers.
05 · Network
The client pays for the completed task, the provider is paid, and the full record appears in the Management App.
GPU capacity


Datacenter
80 GB HBM
Large-model training and high-throughput inference.


Datacenter
40 / 80 GB HBM
Fine-tuning, distributed jobs and batch inference.


Prosumer
24 GB GDDR6X
Image and video generation, rendering, mid-size LLMs.


Edge
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.
Enterprise
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.
Get started
Rent GPUs for your AI workloads, or earn by sharing yours.