How to optimise GPU utilisation and reduce cloud costs

GPU cost optimization is presented as primarily a utilization challenge, since expensive GPUs can incur identical costs whether they are heavily used or mostly idle. Effective management requires monitoring streaming multiprocessor utilization alongside memory use, memory bandwidth, and idle time, then right-sizing GPU types and quantities to workload needs. Recommended practices include consolidating workloads through bin-packing, sharing GPUs with partitioning or time-slicing where suitable, using gang scheduling for distributed training, automatically shutting down idle environments, and scaling inference capacity according to real demand. Shared GPU pools, project quotas, and showback or chargeback systems can improve utilization and make spending accountable across teams. Northflank positions its platform as supporting these practices through per-second billing, autoscaling, scheduled jobs that release resources on completion, spot-instance orchestration, BYOC deployment for applying cloud reservations or committed-use discounts, observability metrics, and project-level access and resource controls.

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