GPU & AI · scheduling and placement

Place AI workloads with capacity and policy in view.

Schedule training, inference and general accelerator workloads according to pool capability, reservation, tenant quota, location and lifecycle state.

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Scheduler · placementVM · m.large × 4→ Hall AKubernetes · 6 nodes→ Hall AGPU job · 4× accelerator→ Hall BHall A · CPU + GPUHall B · acceleratorsgpugpugpugpugpugpugpu18 nodes · 7 accelerators · placement follows quota and residencyVMS · CONTAINERS · KUBERNETES · GPU · STORAGE · NETWORK — ONE CATALOGUE
Input
Workload intent
Service and resource needs
Decision
Policy-aware
Quota, reservation and location
Target
Eligible capacity
Healthy compatible pools
Record
Traceable
Placement retained with service
Scope

Scheduling as an operator policy decision.

Placement considers the customer commitment and the estate state together.

Capability matching

Match service requirements to eligible accelerator pools.

Quota and reservation

Respect shared, dedicated and committed capacity boundaries.

Location policy

Place by site, residency and customer choice.

Lifecycle awareness

Avoid unhealthy or unavailable capacity during placement.

Operating workflow

Resolve each request against real GPU capacity.

The service request carries customer, project and commercial context into placement.

  1. 01

    Read requirements

    Understand accelerator, location and service profile.

  2. 02

    Apply boundaries

    Check tenant quota, reservation and policy.

  3. 03

    Select capacity

    Choose a healthy eligible pool.

  4. 04

    Record placement

    Attach the resulting resource to service and metering.

Two connected responsibilities

Operator control and customer action stay explicit.

Operator responsibility

What influences placement

  • Pool capability and health
  • Reservations and commitments
  • Tenant and project quota
  • Location and lifecycle policy
Service consumer

What the customer selects

  • Approved GPU service
  • Project and workload context
  • Eligible location
  • On-demand or committed plan
Operational outcome

What this changes in the working model.

Outcome 01

Policy consistency

Every placement follows the same enforceable rules.

Outcome 02

Commitment awareness

Reserved and shared capacity remain explicit.

Outcome 03

Operational traceability

Teams can explain why and where a workload was placed.

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Partner with us

Design scheduling around your estate and commitments.

We will map pools, reservations, quotas and placement rules into the GPU service path.

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