Tokens per dollar on a rented GPU

By the token, the price is fixed and the sum is one division. By the hour, you pay for time, and tokens per dollar is whatever you manage to produce in that time.

Take the tokens your setup generates in an hour of real traffic. Divide by the hourly price of the GPU. That is your tokens per dollar on that machine.

A GPU costs the same busy or idle. Serve many requests at once, all day, and its tokens per dollar can pass a per-token API. Leave it idle half the time and the figure halves.

So the comparison is not between two prices. It is between a fixed rate and a figure you earn by keeping the card full.

Throughput depends on the model, the precision you serve it at, the serving software and how many requests you batch together. Measure it on your own traffic. A published tokens-per-second figure for another setup tells you little.

Count the hours you pay for but do not serve: loading the model, testing and quiet nights. And count the work of running it, which a per-token price includes.

Start by the token. It costs nothing while idle and gives you real numbers: tokens per day, the mix, the peak. Those are the inputs to the GPU sum.

Move a workload to a GPU when it is steady and large enough to keep a card busy, or when you need a model or a setting no host offers. Keep uneven work on tokens.

Runcrate does both on one account. Its API serves 124 models by the token. Its GPUs are billed by the second on most models, and booting is never billed, so idle time you stop is time you do not pay for. A ready Inference image shortens setup, and a GPU can be started in about four minutes.

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