Cut LLM inference cost without killing quality

Inference is the process of running an AI model to produce an answer. Its cost depends on the model you use, the amount of text it processes, and how efficiently the service runs.

There are several ways to reduce that cost. The important question is whether the assistant still completes the task well.

Use smaller models selectively

Model routing means choosing a model for each request instead of sending everything to the most powerful one. A simpler request may work well on a cheaper model, while demanding work goes to a stronger model. Research supports this approach, but the savings and quality depend on the tasks being evaluated.

Reduce memory requirements carefully

Quantisation stores model weights, the numerical values learned during training, at lower precision. This can reduce memory requirements and make deployment more efficient. It can also affect answer quality, so test the compressed model on your real tasks. Hardware and serving software influence the result.

Avoid repeated computation

Prefix caching reuses computation for identical text at the beginning of requests, such as repeated instructions. This differs from reusing a complete answer: the model still generates a response to the new request. Efficient memory management can also help a server process more requests at once.

Keep GPUs busy without creating queues

A graphics processing unit, or GPU, is a processor commonly used to run AI models. Serving software can group work from multiple requests to use it more efficiently. But higher throughput is only useful if users still receive answers within an acceptable time.

Your optimisation plan:

1. 1Establish a baseline for task quality, cost, and response time.
2. 2Test a smaller model on suitable tasks.
3. 3Add routing if different tasks need different model capabilities.
4. 4Benchmark quantisation if you control model deployment.
5. 5Tune caching and request scheduling.
6. 6Compare savings against quality and waiting time.

Change one major variable at a time. Otherwise, you may see a cheaper service without knowing which change caused its errors.