From GPU capacity to a usable inference service

Many regional compute and data centre providers have GPU capacity and enterprise customers who want to use AI on infrastructure they trust. The gap is usually software: turning servers into a service that customers can deploy models on and send requests to, without each customer building an orchestration stack of their own.

Software for capacity you already run

Aventail^™ Link Server is the runtime you install on supported provider machines. It deploys and executes models on that hardware. Aventail^™ Control adds central management, so models, configuration and machines can be managed together. Routing then sends customer requests to the right place. Together these give you an inference engine and an orchestration layer that work as one system.

Your infrastructure. One inference and orchestration layer.

Agree the operational responsibilities for your deployment. A typical starting point is:

• You manage the hardware, network, physical and logical access, security controls and the day-to-day operation of your service.

• Aventail^™ software supplies model deployment, inference execution, routing and central management.

What your customers get

Your customers connect to a usable service rather than a set of raw machines. They choose a supported model, deploy it to your capacity and send requests to it. They do not need to be MLOps specialists to get started, although your team will still operate the infrastructure.

Your compute becomes one routing destination among those a customer approves. The same frontend or integration can also reach their own hardware running Aventail^™ Link, or frontier APIs used through their own accounts, under their policy. This lets a customer use your capacity for the work it suits without having to rebuild their applications around a single endpoint. Read how Aventail^™ works.

Check platform and model support first

Deployment and integration details depend on the currently documented platforms and supported models. Model engines, formats and resource requirements vary, so confirm your hardware and target models against versions and supported models before you plan a customer launch. The platform architecture explains how the runtime, routing and management fit together.

A practical onboarding sequence

1. Scope the service: which customers, which models, which hardware and which regions you want to offer.

2. Check compatibility against the documented platforms and supported models.

3. Install Aventail^™ Link Server on your chosen machines. The quickstart guide covers getting started.

4. Enrol the machines in Aventail^™ Control so they can be managed centrally.

5. Deploy an initial supported model and confirm requests run as expected on your hardware.

6. Agree destination and fallback policies with your first customer, including when your compute should be used.

7. Review security settings and reporting with your team. See the security guide and usage analytics.

8. Inspect activity in Aventail^™ Control, then extend to further models and customers.

The commercial case

The benefit is straightforward. Capacity that customers can deploy models on and route requests to is easier to sell than capacity they must assemble into a service themselves. Your enterprise customers can keep suitable work on regional infrastructure they have approved, and you can offer that without building and maintaining a full inference and orchestration stack.

Start with one service you want to offer and the models your customers need. See the Aventail^™ Server page for more.