Five ways to cut inference cost without changing your model

When an AI bill starts to hurt, the first instinct is to look for a cheaper model. Often the bigger savings are in how the current model is served.

1. Batch requests continuously

Serving one request at a time leaves most of a GPU idle. Continuous batching, as implemented in vLLM and TensorRT-LLM, groups requests as they arrive and can multiply throughput on the same hardware.

2. Quantize carefully

Running weights in FP8 or INT4 reduces memory and increases speed. Test quality on your own evaluation set first; for many tasks the difference is negligible.

3. Cache what repeats

System prompts, shared document context and frequent questions repeat constantly. Prefix caching and response caching avoid recomputing them.

4. Route by difficulty

Not every request needs your largest model. A simple router can send easy requests to a small model and escalate only the hard ones.

5. Make idle capacity visible

You cannot reduce what you cannot see. Per-team dashboards and budgets turn GPU spend from a mystery into something teams actively manage.

Measure first. Every change above should be tested against a baseline of cost, latency and quality.

Our GPU Infrastructure Optimization service applies these steps systematically to your environment.