Calculating the total cost of a GPU cluster

Goodput describes the useful work a cluster delivers after accounting for interruptions and lost progress. For a training workload, failures can mean repeating work since the last checkpoint, identifying the faulty node and restarting the job.

The session explains how failure frequency, job size, checkpoint intervals and recovery time affect the cost of useful compute. Two clusters with the same GPU-hour price can deliver different amounts of completed work.

Hot spares allow a job to restart on replacement capacity while the failed hardware is repaired. This can reduce the time a workload spends waiting for its original node to return.

Recovery still takes time: the platform must identify the failure, make replacement capacity available and initialize the job. The session also explains why the number of GPUs affected by a failure matters when estimating its cost.

Storage affects data loading, checkpointing and job initialization. Slow storage can leave GPUs waiting for data or extend the time needed to resume work after a failure.

When comparing providers, consider storage capacity, price and performance together. The calculator walkthrough shows how changing storage requirements affects the overall cluster cost comparison.

A large training job can lose progress across many GPUs when one component fails. Research clusters often run several smaller jobs, so a failure affects a smaller share of the cluster.

Inference services with load balancing and fault tolerance can keep serving requests on healthy capacity while a node is repaired. Recovery costs still matter, but the impact differs from interrupting a large training run. Your TCO assumptions should reflect your actual workload.

Use a consistent workload scenario and make the assumptions explicit: GPU count, average job size, storage requirements, failure frequency, recovery time and engineering effort.

The webinar first holds GPU-hour pricing constant to isolate other cost drivers. It then discusses using actual provider quotes. Jordan also clarifies that zero debugging cost in a comparison can represent a baseline, rather than an expectation that no debugging will be needed.