Inference Cost Optimization
Definition
Optimization of inference costs considers the effort involved in deploying already trained models.
Optimization of inference costs seeks a more cost-effective execution of model queries under given quality requirements. For language model services, FrugalGPT investigates, among other things, the selection and combination of different models.
Simply explained
Like choosing the right vehicle for a job: not every delivery needs the largest truck. Unlike transport, answer quality must be specifically verified.
Why it matters in practice
Before comparing costs, tasks, quality standards, and considered costs should be defined.
Example from business
Possible example: An operation tests which internal text queries a smaller model answers sufficiently.
Limits and pitfalls
- Lower expenses alone do not prove equivalent answers. Savings measured in one study are no guarantee for other tasks.
Origin & history
In 2023, FrugalGPT examined prompt tuning, model approximation, and model cascades for cost-sensitive language model queries, among other things.
Use in mid-sized companies
- Possible application areas are internal text assistants and document processes; multiple execution paths would be compared under the same quality requirements.
Frequently asked questions
Is this about training?
Here the focus is on the deployment of already trained models.
Is the cheapest model automatically suitable?
No, the quality requirements of the task remain decisive.
Sources
- FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance · Chen et al. (2023): FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance
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