AI engineering glossary · Inference serving optimisation
What is reserved capacity?
Reserved capacity (also called Committed use): Cloud computing capacity, such as GPUs, bought or committed for a fixed period at a lower hourly price than on-demand. It lowers cost for steady traffic but leaves you paying for idle hardware if demand falls.
Reserved capacity: an example
A company commits to 4 GPUs for a year for baseline traffic and rents extra on demand for peaks.
Related terms
More Inference serving optimisation terms
- Roofline model
- Scale factor
- Semantic caching
- Semi-structured sparsity
- Service level objective
- SGLang
- Shared memory
- Small language model
Where you learn it
Reserved capacity is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.