- Choose a sharing method from workload and isolation requirements.
- Account for memory, cache and communication as well as compute.
- Use quotas and admission controls before a pool becomes congested.
- Keep allocation state visible to service owners and operators.
Accelerators are expensive and demand rarely arrives in a smooth pattern. One team may need a complete device for training, while another needs a small and steady inference service. A shared pool can make capacity available to more work and reduce time spent waiting for a new purchase.
Sharing also creates competition for memory, compute, network and storage. If the platform hides this competition, users see unstable performance and operators cannot explain capacity. A clear sharing model defines what is isolated, what is shared and how the system responds when demand exceeds the limit.
Classify the workloads first
Long training jobs, interactive inference, notebooks and short batch tasks have different needs. Training can use full devices and fast links for hours. Interactive inference needs predictable latency and enough cache for active contexts. Development work can tolerate interruption but may need access to a specific software stack.
Teams should record memory, compute, communication, duration and service-level needs for each class. Security and data rules also matter. Workloads from different trust boundaries may require stronger separation than workloads from one controlled team. This classification guides both hardware and scheduler policy.
Whole-device allocation is the simplest boundary
Allocating a complete GPU gives the workload a clear resource and avoids many forms of interference. It works well for training, performance tests and services that use most device memory. Scheduling can pack jobs across nodes while preserving topology requirements for multi-device work.
The cost is unused capacity when a workload needs only part of the device. Queue time can also grow when large devices are reserved for small tasks. Whole-device scheduling remains a good baseline because behavior is easy to measure and recovery is direct. More complex sharing should show a clear benefit over this baseline.
Hardware partitions provide defined slices
Supported accelerators can use Multi-Instance GPU partitions to divide one device into isolated compute and memory instances. Each instance appears as an allocatable resource with a defined profile. This can fit several smaller inference or development workloads on one physical device.
Profiles are fixed and availability depends on device generation and current partition layout. Reconfiguring a device can interrupt existing work. The platform must track parent devices, partition profiles, health and placement. Monitoring should preserve both the tenant view and the physical device view.
Time sharing increases use but weakens predictability
Time sharing lets several processes use one device without a hard memory partition. It can work for bursty or low-priority tasks, but one process can consume memory or produce long kernels that affect others. Application crashes and memory pressure may also have a wider effect.
A serving system can share capacity at a higher layer by batching requests inside one trusted model process. This often provides better cache and scheduling control than unrelated processes on the same GPU. However, the service must enforce tenant quotas, request limits and data separation.
Scheduling needs topology and admission control
A scheduler must know more than the number of free devices. It should account for memory, partition profile, fabric locality, model residency and current service load. Kubernetes Dynamic Resource Allocation provides a framework for describing and claiming specialized resources, while device-specific drivers expose available configurations.
Admission control protects the pool before it reaches failure. The platform can reject or queue work that cannot meet its limit, reserve capacity for critical services and apply team quotas. Chargeback or showback data should use allocated and delivered service capacity, not only device ownership.
- Set maximum context, batch and active-token limits for inference.
- Keep test jobs away from critical low-latency services.
- Alert on memory pressure and allocation fragmentation.
- Test device and worker recovery for every sharing mode.
How NAIM keeps allocation explicit
NAIM represents connected nodes and workload planes through desired state. Operators can select where a service runs, which model artifact it uses and which runtime settings apply. This controlled target reduces hidden manual placement and gives teams a consistent path to restart or move a workload.
Chainzano uses this model with a capacity design that includes accelerator topology, network and storage. We choose the simplest sharing method that meets the service target, then validate isolation and performance under competing load. The result keeps improved use measurable and reversible.
Make service policy fair and predictable
A shared pool needs rules that users can understand. Priority should follow business service classes, not the order in which teams learned to bypass the scheduler. Quotas can cover devices, active jobs, tokens, memory or time. The platform should show the current allocation, queue position and reason for rejection without exposing another tenant's sensitive data.
Policies also need review. Reserved capacity that remains unused for months may be released to preemptible work, while a new critical service may need a protected minimum. Changes should use measured demand and service results. A transparent review process reduces conflict between teams and helps management decide whether software policy, schedule changes or new hardware provide the best next step.
Preemption requires its own rules. The system should state which work can stop, how much notice it receives and whether it can resume from a checkpoint. Users must know the risk before selecting a lower-cost class. The platform should measure lost work so a policy that appears efficient does not create hidden waste. Periodic tests should confirm that checkpoint restore works on currently available capacity. Results and failures should be readily visible together in the regular shared capacity service review.
