AI workloads are already costly because of the excessive value of renting GPUs and the related vitality consumption. Reminiscence bandwidth points make issues worse. When reminiscence lags, workloads take longer to course of. Longer runtimes end in larger prices, as cloud providers cost based mostly on hourly utilization. Basically, reminiscence inefficiencies enhance the time to compute, turning what must be cutting-edge efficiency right into a monetary headache.
Do not forget that the efficiency of an AI system isn’t any higher than its weakest hyperlink. Irrespective of how superior the processor is, restricted reminiscence bandwidth or storage entry can limit total efficiency. Even worse, if cloud suppliers fail to obviously talk the issue, prospects won’t understand {that a} reminiscence bottleneck is lowering their ROI.
Will public clouds repair the issue?
Cloud suppliers at the moment are at a important juncture. In the event that they need to stay the go-to platform for AI workloads, they’ll want to deal with reminiscence bandwidth head-on—and shortly. Proper now, all main gamers, from AWS to Google Cloud and Microsoft Azure, are closely advertising the newest and best GPUs. However GPUs alone gained’t remedy the issue until paired with developments in reminiscence efficiency, storage, and networking to make sure a seamless information pipeline for AI workloads.
