AllocateWhat is GPU utilization in AI inference?
GPU utilization measures how much of expensive accelerator hardware does useful work rather than sitting idle. Naive self-hosted inference commonly runs below 15% utilization; optimized platforms with batching, routing, and multi-tenant scheduling run several times higher, which is where most inference cost savings actually come from.
Utilization is why buying GPUs is rarely the answer for a single company’s workload: traffic is spiky, and idle silicon burns money. Shared, metered platforms pool demand and pass the efficiency back as lower per-token prices.
When teams do need dedicated capacity for isolation, the same techniques (batching, quantization, right-sized models) decide whether it is affordable.
Try it: Self-hosted vs API break-evenRelated terms
Allocate is the cloud inference platform for companies that want to train and run their own models.