The Cheapest LLM Call Is the One You Don't Await

The text introduces a new type of API call known as an asynchronous request for handling large language model (LLM) inference, which offers a cost-effective alternative to synchronous and batch processing. While synchronous requests are expensive due to the necessity of keeping GPUs ready at all times, and batch processing may not suit all tasks, asynchronous requests allow for scalable, fire-and-forget calls that fill idle GPU times, reducing costs and eliminating the need for strict rate limits. These requests are particularly beneficial for tasks where immediate responses are not crucial, such as large-scale content generation, user-profile enrichment, and background tagging. Asynchronous requests enable developers to set up webhook endpoints to receive responses without waiting, thus optimizing server resources and lowering costs, with Inference.net offering these requests at a 10% discount compared to synchronous rates. The approach is highlighted as a practical solution for managing large workloads efficiently without compromising on performance or incurring high costs, especially for tasks that do not require instant outcomes.

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