Unified GPU Access
A consistent path to GPU infrastructure across multiple providers.
GLOBAL AI INFRASTRUCTURE
Global Compute.
One Platform.
ComputeNexa is building a unified way for AI teams to discover and access GPU compute across infrastructure providers and regions.
Now in early access A GPU with Jupyter, PyTorch and your Kaggle / Hugging Face tokens ready in about a minute.
COMPUTENEXA NETWORK
CN
Connecting workloads to distributed compute
OUR MISSION
Make high-performance compute easier to access.
GPU infrastructure is expanding rapidly around the world, but capacity remains fragmented across cloud providers, data centers, regions, and platforms.
ComputeNexa is working to simplify this ecosystem through a unified platform—helping businesses, developers, and AI teams connect with the compute resources their workloads require.
WHAT WE'RE BUILDING
Infrastructure designed around choice.
Global Compute Network
Connecting infrastructure providers and GPU capacity across regions.
API-First Infrastructure
Built with programmatic provisioning and modern AI workflows in mind.
Intelligent Routing
A vision for matching workloads by GPU type, geography, availability, performance, and economics.
GET STARTED
Rent your first private GPU in minutes.
Each GPU is reserved for your team and runs open AI models or your own code, billed by the minute from prepaid credits. Stop early and unused time is refunded. Use it from the dashboard, our API, the Python SDK or the command line.
pip install computenexa
Getting started
Pick a GPU and an app, launch it, connect in the browser or over SSH, and stop it when you're done.
Set up your team
One shared balance for your company, everyone with their own login, and owners and admins in control.
Launch from code
The Python SDK, the cnx command-line tool and the API: launch and stop private GPUs from scripts, CI or your platform.
All guides
Every guide and the full API reference, in one place.
FOR AI TEAMS
Planning an AI workload?
Tell us what you need. We want to help teams identify suitable infrastructure options for demanding AI workloads.
Talk to ComputeNexa →Model trainingInferenceFine-tuningBatch processingGPU clustersCustom AI workloads
CASE STUDY
A private contract analyst on one GPU.
We reviewed 225 contracts with an open 14B model on a single RTX 5090 and compared it with a frontier API: 88% on commercial-contract risk flags, 2.4 points behind, at about a twenty-seventh of the cost. Plus an honest look at why NDAs are harder.
OUR VISION
Compute should not be constrained by a single cloud provider or geography.
We envision an intelligent global compute layer where workloads can connect to suitable infrastructure based on their technical and economic requirements.