Beat the GPU Storage Bottleneck for AI and ML

Data centers that support AI and ML deployments rely on Graphics Processing Unit (GPU)-based servers to power their computationally intensive architectures. Across multiple industries, expansion in GPU use is behind the over 31 percent CAGR in GPU servers projected through 2024. That means more system architects will be tasked to assure top performance and cost-efficiency from GPU systems. Yet optimizing storage for these GPU-based AI/ML workloads is no small feat. GPU servers are highly efficient for the matrix multiplication and convolution required to train large AI/ML datasets.