Your model. Your GPU capacity. One endpoint.
Deploy a public, private, or fine-tuned model on dedicated GPU infrastructure. OneInfer handles provisioning, runtime configuration, scaling, and endpoint access from a single console.
Dedicated endpoint
Production deployment
Choose a model
Use a supported template or your private model.
Configure compute
Select GPU, provider, region, workers, and concurrency.
Deploy and call
Send requests through your stable OneInfer endpoint ID.
What is a dedicated endpoint?
It is an isolated model deployment backed by GPU resources configured for your workload. Unlike a shared Model API, its capacity and runtime settings belong to your deployment.
Dedicated capacity
Your requests are sent to the GPU workers assigned to your deployment, avoiding contention with shared API traffic.
Predictable configuration
Control GPU type and count, region, worker limits, concurrency, storage, and scaling behaviour for each endpoint.
Private model support
Deploy supported templates, Hugging Face models, or a custom model source without exposing the model through a public catalog.
From model selection to a callable API
OneInfer manages the deployment lifecycle while giving your team direct control over the settings that affect performance and cost.
Choose the model runtime
Start from a supported deployment template or configure a compatible model source for your use case.
Select GPU and region
Choose available compute by provider, GPU type, GPU count, and deployment region instead of managing cloud instances yourself.
Set workers and concurrency
Define minimum and maximum workers, scaling mode, and request concurrency to match your traffic pattern.
Provision from one console
Create, inspect, and delete deployments from the OneInfer console without maintaining a separate infrastructure workflow.
Call it through OneInfer
Use your OneInfer API key and endpoint ID to direct inference requests to the exact deployment you created.
Build an intelligent endpoint
Attach multiple dedicated or serverless endpoints behind one intelligent endpoint for routing, balancing, and failover policies.
Choose the right way to run inference
READ THE DOCS ›Model APIs for instant access
Use shared, pay-as-you-go inference when you want to prototype quickly or your traffic is still variable.
EXPLORE MODEL APIS ›Dedicated endpoints for control
Use reserved deployment capacity when you need a private model, stable runtime configuration, or predictable production performance.
CREATE ENDPOINT ›One endpoint for multiple backends
Group dedicated and serverless endpoints, then route requests according to latency, cost, load, or model policies.
EXPLORE ROUTING ›