AI workloads are already costly as a result of excessive value of renting GPUs and the related power consumption. Reminiscence bandwidth points make issues worse. When reminiscence lags, workloads take longer to course of. Longer runtimes end in increased prices, as cloud providers cost primarily based on hourly utilization. Primarily, reminiscence inefficiencies improve the time to compute, turning what needs to be cutting-edge efficiency right into a monetary headache.
Keep in mind that the efficiency of an AI system is not any higher than its weakest hyperlink. Regardless of how superior the processor is, restricted reminiscence bandwidth or storage entry can limit general efficiency. Even worse, if cloud suppliers fail to obviously talk the issue, clients won’t understand {that a} reminiscence bottleneck is lowering their ROI.
Will public clouds repair the issue?
Cloud suppliers are actually at a important juncture. In the event that they wish to stay the go-to platform for AI workloads, they’ll want to handle reminiscence bandwidth head-on—and rapidly. 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 knowledge pipeline for AI workloads.
