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

Category: ai-ml

Tags: ai, queueing-theory, simulation, throttling, inference

Score: 5.3/10 (Innovation: 7, Technical: 5, Documentation: 2, Utility: 4)

This project demonstrates via simulation that throttling AI models under load can backfire by increasing retry demand, using queueing theory and dynamic programming to find optimal policies. It includes a toy Flask+JS demo for policy comparison and an accompanying paper with proofs. The insight is interesting for inference infrastructure but the project is minimal and lacks documentation.

Target audience: AI infrastructure engineers, ML platform teams, and researchers in systems for machine learning

Repository: https://throttle.staffinganalytics.io/?src=hn2

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