Infrastructure guide
GPU cluster readiness
A practical review of compute, orchestration, failure handling and throughput.
Start with the work
State the framework, model, target quality, dataset size, checkpoint size and deadline. For a distributed job, specify the minimum useful scale and what a failed node does to the run. An accelerator label alone does not describe delivered work.
Ask for a topology and a run record
Record accelerator type, GPUs per node, interconnect, placement boundaries, storage throughput and data locality. Ask for a reproducible run using a representative dataset, training recipe and target quality. Define effective work as useful progress against that recipe.
Plan recovery before reserving capacity
Check checkpoint cadence, restore time, isolation, node replacement and queue delays. Kubernetes GPU scheduling depends on drivers and device plugins. A cluster may also use Slurm or another scheduler, which changes the operational checklist.
Compare under a shared workload definition
Compare two cluster plans with one workload and deadline, then read the benchmark note before accepting a throughput claim.