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Batch size sets the trade-off between speed and cost in AI inference. An AI server answers many users at once by bundling their requests into one batch. Our $CBRS note compares this to running a bus. There are two ways to run it, and each gives something up. 1. Fill the bus. Put as many passengers as possible on every run. More stops slow each passenger's trip, but the cost of the run is spread across more people, so the cost per passenger falls. Larger batches do the same: each user's answer is slower, and the cost per token falls because the system produces more tokens from the same hardware. 2. Carry fewer passengers and stop less. Each trip is faster, but fewer seats mean the cost of the run is spread less well, so the cost per passenger rises. Smaller batches are fast for one user and raise the cost per token. So in the analogy, speed for each user is how fast each passenger arrives. Cost per token is cost per passenger. A system set up for one gives up some of the other. When someone compares two inference systems, check the batch size behind each number. A speed figure or a cost-per-token figure quoted without it cannot be compared fairly.

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