GPU Capacity Planning and Allocation
Explore how AI teams can request GPU capacity, understand queues and placement rules, and review usage across shared infrastructure.
Make capacity requests and constraints clear
Explain why workloads wait or need different resources
Connect usage with the teams requesting it
Video walkthrough · An illustrative GPU-capacity workflow using sample requests, queues and usage records. Open video
- Industry
- Technology
- Focus
- GPU orchestration
- Inputs
- Capacity inventory, workload needs and allocation policies
- Teams
- AI platform, infrastructure and research-computing teams
The business situation
Where this fits
AI teams need compute, while infrastructure owners need to balance capacity, compatibility and competing priorities. An available GPU is only useful when the workload can run on it and the allocation rules are clear.
For organisations deciding how several teams should request and share scarce GPU capacity.
- Teams cannot see why a workload is waiting.
- Capacity requests omit important runtime requirements.
- Shared usage is difficult to explain by project.
The solution
How the solution helps
A shared GPU-service workflow starts with the workload’s requirements and the capacity available. It brings queue, quota and placement questions into a reviewable process, then connects usage with the requesting team. The selected hardware and runtime combinations define the scope.
Describe the workload
Capture the compute, runtime and policy requirements of the request.
Review capacity and placement
Make compatibility, quota and queue conditions visible.
Understand team usage
Connect allocated capacity and usage information with the requesting project.
In practice
Review a request for GPU capacity
Explore a team’s request from workload requirements to allocation review.
A workload description, compatible capacity inventory, project priorities and quota rules.
Capture the requirement
Record what the workload needs to run successfully.
OutputA clear request with compatibility requirements.
Explain the placement options
Compare eligible capacity with queue and quota conditions.
OutputA reasoned allocation or waiting decision.
Review project usage
Connect the selected capacity and usage information with the requesting team.
OutputA basis for capacity and spending discussions.
How it works
How the workflow comes together
Capacity view
Identify the resource pools and runtime combinations included in the service.
Allocation policies
Define how requests are admitted, prioritised and reviewed.
Usage view
Link the chosen usage and cost information to projects and owners.
- Capacity inventory
- Workload requirements
- Queue and quota policies
- Project usage attribution
Customer value
What this means for your team
A clear path to shared compute
A structured request and allocation process makes the relationship between workload needs, capacity and policy easier to understand.
More predictable capacity planning
Help platform teams explain queues, set expectations and discuss capacity needs with the teams using the service.
Review and control
Keep people in control
- Check workload compatibility for the selected hardware and runtime.
- Make queue priorities and quota rules explicit.
- Keep access and spending decisions with the responsible platform owners.
Get started
Start with your use case
Start with one resource pool and the requests that are most difficult to prioritise today.
Useful starting information
- One selected GPU environment
- Representative workload requirements
- Owners for access, quota and capacity decisions
Success measures to track
- Clarity of request requirements
- Reasons for waiting or rejection
- Visibility of usage by project
Fit with your existing systems
Agree the resource inventory, scheduler and usage sources for the selected environment, including supported workload combinations.
Your next conversation
What stops your AI teams from getting the right GPU capacity?
Tell us how your team works today. We’ll explore the information, integrations and review process that fit your use case.
Discuss this use case