Large clusters for small models — Daniel Svonava, Superlinked
Daniel Svonava from Superlinked shows how to run many small AI models efficiently on ordinary hardware instead of expensive cloud services.
A single GPU from a few years ago can run a small model fast, and for specific tasks—like reading Vietnamese receipts or reviewing contracts—these small models perform as well as larger ones. The problem is serving many models at once: existing tools are unoptimized and routing systems built for one big model become slow when handling many small requests. Superlinked solves it with a system where a gateway sends requests to a shared queue, and workers fetch and process them themselves—doubling throughput. A Rust tool manages different model variants, and the system tunes models automatically; one example cost 80 cents and improved German legal text by 18 percent.
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