Throttling AI models under load can backfire and increase demand (SIM)

Show HN: Throttling AI models under load can backfire and increase demand (SIM)

Throttling AI models under load can backfire and increase demand (SIM) is a company in show hn launch.

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OP here: TL-DR: When AI providers silently swap weaker models under high demand, this can cause even more demand as users tend to re-ask. This is even worse for agents. Both of these effects cause even higher load on the data centers. I guess all of us have felt when the models "don't feel quite the same", so this could explain part of it.I modeled this as a fleet scheduling problem using Queueing Theory and Dynamic Programming over a finite horizon. The standard practice of throttling once the number of jobs in server exceeds certain threshold is in fact suboptimal. The optimal policy consists in segmenting the part of the traffic that is retry sensitive, from those that are not. For exampl

Within the show hn launch space, the company focuses on its core offering rather than a broad product line.

Its current recorded stage is Show HN launch.

Website: throttle.staffinganalytics.io/?src=hn2