Predict, pack, and rebalance your GPU fleet.

Paladin predicts workload resource needs and uses those estimates to bin-pack compatible jobs into available GPU capacity. It continues to rebalance as demand changes, so the fleet can accommodate growing workloads and queued jobs.

Resource prediction

Anticipate what a workload will need rather than treating its initial allocation as permanent. Check forecasts against measured use across the workload's phases.

Bin packing

Fit compatible jobs together across GPUs and clusters. Placement considers the capacity each job needs; shared GPU memory has an enforced quota for each workload.

Continuous rebalancing

When demand changes, Paladin can move supported running work to another GPU with its state preserved. Placement and migration work together to keep available capacity useful.

Compatible training and inference jobs can share a GPU. Resource quotas and runtime isolation control each workload's access to that shared capacity.

Memory quota

Allocation percentages represent a share of GPU memory. Paladin enforces the quota at runtime; actual memory use is measured separately. Neither percentage is a measure of compute utilization.

Isolation

Paladin enforces workload isolation at runtime. Qualify compute interference between neighboring jobs and your team's resource requirements on the target fleet.

Paladin checkpoints, moves, and resumes supported workloads between GPUs and clusters with their full execution state preserved. A growing workload can move into more capacity while its neighbors keep running.

Reserve, checkpoint, transfer, resume

Reserve capacity on the destination, capture the running workload's state, transfer it, and resume the same workload there. Verify restoration with the runtime and distributed configuration you plan to deploy.

Resizing and migration

Expanding a memory quota on the same GPU changes the allocation. Migration moves the running workload to another GPU. Starting a new job elsewhere is a placement decision.

Interruption

State preservation avoids starting the job over. Establish the checkpoint and resume points and measure the migration pause against your workload's latency requirements.

Failure recovery

Agree how the job resumes if the destination becomes unavailable or a checkpoint cannot be restored.

Use Paladin independently on your infrastructure or dedicated Cumulus GPUs. Team permissions, quotas, and usage visibility let you share one fleet while retaining control over who can use it.

Fleet

Provide the GPU types, device memory, host memory, interconnects, and existing scheduling environment. Confirm the supported sharing and migration paths across those devices and clusters.

Team boundaries

Specify resource quotas, permitted workloads, and the isolation your teams need.

Application layers

Paladin orchestrates GPU capacity. Add Ion for model inference or Talos for agents when you need those layers; they are not prerequisites for adopting Paladin.

Use the same hardware, job mix, arrival pattern, and runtime as your current environment. Compare completed work, queue time, active GPU compute, and memory use across the full predict–pack–rebalance cycle.

Prediction and placement

Check predicted demand against measured use, including peaks and changing phases. Measure whether packed jobs complete within their service targets when sharing a device.

Migration and recovery

Verify workload identity and execution state after migration. Measure checkpoint, transfer, and resume time, and exercise a destination or restoration failure.

Useful utilization

Track completed work and service targets alongside device activity. More allocated memory alone does not demonstrate more useful GPU work.