"AI workloads are already expensive due to the high cost of renting GPUs and the associated energy consumption. Memory bandwidth issues make things worse. When memory lags, workloads take longer to process. Longer runtimes result in higher costs, as cloud services charge based on hourly usage. Essentially, memory inefficiencies increase the time to compute, turning what should be cutting-edge performance into a financial headache."
"Remember that the performance of an AI system is no better than its weakest link. No matter how advanced the processor is, limited memory bandwidth or storage access can restrict overall performance. Even worse, if cloud providers fail to clearly communicate the problem, customers might not realize that a memory bottleneck is reducing their ROI. Cloud providers are now at a critical juncture. If they want to remain the go-to platform for AI workloads, they'll need to address memory bandwidth head-on-and quickly."
AI workloads incur high costs because of expensive GPU rentals and significant energy consumption. Memory bandwidth limitations slow data movement, extending runtimes and raising hourly cloud charges. Slower memory and storage access can negate advances in processors and GPUs by becoming the performance bottleneck. Lack of clear communication from cloud providers can leave customers unaware that memory constraints are reducing their ROI. Cloud providers must urgently improve memory performance, storage, and networking to deliver a seamless data pipeline and preserve their position as primary platforms for AI workloads.
Read at InfoWorld
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