When to Use GPU Quotas, Reservations, Priorities and Preemption

GPU quotas, reservations, priorities, and preemption solve four different scheduling problems. Quotas limit how much a tenant or project can consume. Reservations protect capacity for planned work. Priorities decide which workload should receive scarce capacity first. Preemption allows a higher-priority workload to reclaim resources from lower-priority work when the policy permits it.

The source scheduling model supports tenant quotas, task queues, priority, preemption, checkpoint protection, resource reclamation, and visible queue reasons. In practice these controls work best together, because no single mechanism solves every capacity problem.

What is a GPU quota?

A GPU quota sets the resource boundary for a tenant, project, or other approved ownership scope. The source scheduling design describes tenant quotas as a hard constraint that prevents disorderly competition for expensive accelerator resources.

According to the source Q&A, quotas can be defined along several dimensions, and the exact model depends on the organization's management policy:

• GPU quantity
• Accelerator memory
• CPU and memory
• Concurrent tasks
• Monthly accelerator card hours

A quota is useful when several teams share one infrastructure pool and the platform needs to stop one team from consuming all available capacity. It also helps with budget control: a project may be technically allowed to run on the shared cluster but limited to a defined amount of capacity or monthly consumption.

When should GPU quotas be used?

Use quotas when you need a hard boundary between tenants, projects, or teams. Typical situations include multiple departments sharing one GPU cluster, external or internal tenants that need isolated resource limits, and projects with approved capacity budgets. They also apply when a development team should not consume production capacity without authorization, or when monthly accelerator usage must stay within an approved budget.

Quotas help when demand is unpredictable, too. Without a limit, a burst of submitted jobs from one project can fill the queue and consume the entire resource pool. That is why the source model puts quota validation before queue scheduling: a task first has to be allowed to request the resource, and only then should priority and scheduling policy decide when it receives it.

What is a GPU reservation?

A reservation protects capacity for a planned workload, project, service, or time window. The source materials explicitly distinguish reserved capacity from free and deployable capacity in planning, although they do not prescribe one universal reservation product or algorithm. Operationally, a reservation means some capacity is committed before it is actively consumed.

Say a critical training project needs 32 GPUs tomorrow night, a production inference service needs guaranteed headroom for peak traffic, or a hardware validation program requires a specific accelerator class during a maintenance window. In each case the reserved capacity should not appear as generally free to other workloads if using it would jeopardize the commitment.

That makes a reservation different from a quota. A quota says, "You may consume up to this amount." A reservation says, "This amount is being held for this approved purpose."

When should reservations be used?

Use reservations when the cost of not having capacity at the required time is high. They make sense for scheduled training runs, critical production inference, known business events, migration windows, large model evaluation, hardware validation, and capacity committed to a customer or tenant.

Do not reserve capacity unnecessarily, because reserved but unused GPUs can become expensive idle capacity. The operations dashboard should show reserved capacity separately from installed, allocated, free, and deployable capacity. Intentional protection then stays visible, and nobody misclassifies every reserved card as waste.

What is GPU priority?

Priority determines the order in which competing workloads are considered for resource allocation. The source scheduling model supports task queues with priority and makes queue reasons visible. A higher-priority job can move ahead of a lower-priority job when both are waiting for the same constrained resource.

Priority matters because workloads differ in business importance. Production inference may rank above experimental fine tuning, a customer-facing recovery task above a development benchmark, and a deadline-sensitive training job above a background batch job.

Priority does not necessarily interrupt work that is already running, and that is where it differs from preemption.

When should priority be used without preemption?

Use priority alone when waiting order matters but interruption is too risky or expensive. This is common for long-running training tasks.

Suppose two jobs need the same accelerator class. Job A is already running when Job B arrives with higher business priority. If Job A cannot checkpoint safely, interrupting it may lose many hours of compute. A priority-only policy lets Job A finish while placing Job B ahead of other queued work, so the organization gets a business-aware queue without unnecessary training loss.

The source scheduling design explicitly notes that high-priority preemption must be balanced against protection of running tasks, which is why priority and preemption should be separate controls.

What is GPU preemption?

Preemption allows the scheduler to reclaim resources from one running workload so another workload can use them. The source scheduling model supports high-priority preemption and links it with checkpoint protection and graceful task recovery. It is useful when capacity is scarce and some workloads are explicitly allowed to be interrupted.

For example, a low-priority batch training task is running when a high-priority incident-recovery workload arrives. The lower-priority task supports checkpointing, so the platform saves or uses the latest checkpoint, stops the task, releases the GPUs, and allocates them to the higher-priority work. Later, the interrupted task can resume. Preemption therefore depends heavily on how recoverable the workload is.

When should GPU preemption be used?

Use preemption when all three conditions are true. First, the higher-priority workload has a meaningful reason to start sooner. Second, the lower-priority workload is approved for interruption. Third, the interrupted work can be protected well enough that the cost is acceptable.

Good candidates include interruptible batch jobs, jobs with frequent checkpoints, development workloads, lower service tiers, and flexible background processing.

The source design specifically recommends distinguishing preemptible and non-preemptible tasks. That distinction belongs in the workload submission policy, so nobody has to decide it for the first time during an incident.

When should preemption be avoided?

Avoid or heavily restrict preemption for workloads where interruption creates disproportionate loss:

• Long-running training with no valid checkpoint
• Critical production inference with no redundancy
• Stateful workloads that cannot restart safely
• Jobs where rescheduling changes an approved experimental condition
• Tasks with external dependencies that cannot be reconstructed

The source Q&A warns that high-priority preemption can cause training loss and recommends checkpoint and graceful termination mechanisms. High priority does not automatically mean a job is safe to preempt. Priority expresses importance, while preemptibility expresses recoverability and policy, and a scheduler needs both.

How do quotas and priorities work together?

Quota decides whether the workload is entitled to request the resource. Priority decides its place among eligible workloads.

Take two projects, each with a quota of 16 GPUs. Project A is already using all 16 when a new Project A job asks for 8 more. The job can be rejected or queued because the project quota is exhausted, and its priority does not override the quota unless the organization explicitly defines such a policy.

Now imagine both projects are within quota and waiting for the same GPU class. Here priority can determine which job is served first. Keeping those controls separate makes scheduling explainable.

How do reservations and quotas work together?

Reservations protect a known amount of future or guaranteed capacity, while quotas limit overall consumption, and a project can have both. If Project A has a quota of 64 GPUs and a reservation of 32 GPUs for tonight, it can consume up to 64 according to policy, but 32 are specifically protected for the planned run.

The source materials do not define a universal interaction rule between reservation and quota, so that relationship should be specified in the enterprise scheduling policy. Whatever the rule, reservations have to remain visible so supposedly free capacity is not double-promised.

How do reservations and priorities work together?

A reservation should normally protect the committed resource independently of ordinary queue priority. Otherwise it is not a real guarantee.

Enterprises may still define emergency override policies. The source materials do not prescribe such an override model, so the safer design is to make the policy explicit and auditable. If a reserved pool is overridden, the platform should record who authorized it, which workload used the capacity, which reservation was affected, and what service risk was accepted. This matters most when the reservation belongs to another tenant or a customer commitment.

How does checkpointing change preemption policy?

Checkpointing reduces the cost of interruption. The source scheduler includes checkpoint protection, fault isolation, and resume from checkpoint.

A workload with a recent checkpoint can release its resources with less lost work, which makes it a better preemption candidate. A workload whose last checkpoint is six hours old is much more expensive to interrupt. The scheduling policy can therefore consider checkpoint state in addition to static priority. The source does not define a specific scoring algorithm; what it supports is the principle that running-task protection and checkpoint capability should influence preemption decisions.

For failure recovery, how AI infrastructure can automatically recover training jobs after a GPU or server failure explains the same checkpoint mechanism from the resilience side.

How should queue reasons be shown?

The source design says queue reasons should be visible item by item. That matters when quotas, reservations, priorities, and fragmentation all exist at the same time. A job can be waiting for any of these reasons:

• Quota exhausted
• Matching GPU type unavailable
• Reserved capacity unavailable to this project
• Higher-priority work is ahead
• Required topology is unavailable
• Healthy capacity is insufficient
• Resource fragmentation prevents placement

The user should see the actual reason. Otherwise the same queue can look like a capacity problem when it is really a policy problem.

How do health checks affect these policies?

The source scheduling architecture includes card-level health as an input to resource allocation. Quota and reservation numbers should therefore refer to usable, healthy resources rather than installed cards only.

If a tenant has a reservation for eight GPUs and one reserved card becomes degraded, the platform needs to identify the shortfall and find suitable replacement capacity where policy allows. A degraded card should not remain schedulable merely because a reservation expects it. Health-aware scheduling makes the promises more realistic.

For the card-level model, how enterprises can monitor GPU health, ECC errors, temperature, power consumption, and degraded accelerator cards explains how health state should affect scheduling.

How should enterprises choose between the four controls?

Use the control that matches the problem: quota for entitlement and consumption boundaries, reservation for guaranteed or planned capacity, priority for waiting order, and preemption for controlled interruption when urgent work must reclaim capacity.

Do not use priority as a substitute for quota, and do not use reservations for every workload. Do not enable preemption without a recovery plan, and do not assume a large quota guarantees immediate capacity.

A platform example that combines tenant quotas, priority, preemption, health-aware scheduling, and resource recovery is Sensaka.

If I were defining the scheduling policy, I would make every submitted workload declare four things: who owns it, what quota applies, what priority it has, and whether it can be preempted safely. Reservations can then be added for workloads that need guaranteed capacity at a specific time. That produces a scheduling model users can understand and operations can audit.