# What 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.

## See also

- [Self-hosted vs API break-even](https://allocate.network/tools/self-hosted-vs-api-calculator)

## Related terms

- [Inference](https://allocate.network/glossary/inference.md)
- [Tokens](https://allocate.network/glossary/tokens.md)
- [Quantization](https://allocate.network/glossary/quantization.md)
- [Quantization formats (GGUF, AWQ, FP8)](https://allocate.network/glossary/quantization-formats.md)
- [GPU memory bandwidth](https://allocate.network/glossary/gpu-memory-bandwidth.md)

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