Gaining new insight to grow the business is a strong driver for adding artificial intelligence (AI) and deep learning to an organization’s IT capabilities. Organizations that don’t adopt these cognitive technologies to gain an advantage risk losing out to the competition. Many take the first step by experimenting with AI software on their existing infrastructure.
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Nevertheless, at some point they are likely to “hit the wall”—that is, they run out of infrastructure performance in much the same way even a well-conditioned marathoner may run out of energy before reaching the finish line. According to a recent IDC survey, 77.1 percent of respondents say they ran into one or more limitations with their on-premises AI infrastructure. And 90.3 percent of users running cognitive technology in the cloud ran into these same kinds of limitations.
AI performance challenges
AI and deep learning are extremely demanding on server infrastructure. They require powerful parallel processing, and we think investigating new solutions during the early experimental phase of AI development is critical for infrastructure teams. The same IDC survey shows that businesses take a variety of paths as they carry out experimentation. For example, some develop their solution in a virtual machine (VM) and then migrate to a dedicated server. Others start a proof of concept (PoC) on a partition of a scale-up system and then opt to move to a server cluster.
We believe that choosing the right server hardware plays a decisive role. According to the IDC white paper cited above, responses from businesses running AI applications indicate that a cluster of single and dual-socket servers with high, per-core performance and I/O parameters combined with accelerators such as GPUs are well suited as infrastructure configuration for cognitive applications. Scaling these accelerated compute nodes is not as straightforward as just scaling CPUs. As a result, businesses need to look for a server vendor that is knowledgeable about scaling for AI applications. […]