Since 2018, the consortium MLCommons has been working a kind of Olympics for AI coaching. The competitors, known as MLPerf, consists of a set of duties for coaching particular AI models, on predefined datasets, to a sure accuracy. Primarily, these duties, known as benchmarks, take a look at how effectively a {hardware} and low-level software program configuration is ready as much as prepare a selected AI mannequin.
Twice a 12 months, firms put collectively their submissions—normally, clusters of CPUs and GPUs and software program optimized for them—and compete to see whose submission can prepare the fashions quickest.
There isn’t any query that since MLPerf’s inception, the cutting-edge {hardware} for AI coaching has improved dramatically. Through the years, Nvidia has released four new generations of GPUs which have since change into the trade normal (the most recent, Nvidia’s Blackwell GPU, is just not but normal however rising in reputation). The businesses competing in MLPerf have additionally been utilizing bigger clusters of GPUs to sort out the coaching duties.
Nonetheless, the MLPerf benchmarks have additionally gotten more durable. And this elevated rigor is by design—the benchmarks are attempting to maintain tempo with the trade, says David Kanter, head of MLPerf. “The benchmarks are supposed to be consultant,” he says.
Intriguingly, the info present that the large language models and their precursors have been growing in measurement sooner than the {hardware} has stored up. So every time a brand new benchmark is launched, the quickest coaching time will get longer. Then, {hardware} enhancements regularly carry the execution time down, solely to get thwarted once more by the subsequent benchmark. Then the cycle repeats itself.
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