Will machine learning save the enterprise server business?

Nvidia and server makers Dell EMC, HPE, IBM and Supermicro announced enterprise servers featuring Nvidia’s Tesla V100 GPU. The question is, can servers designed for machine learning stem the erosion of enterprise server purchases as companies shift to PaaS, IaaS, and cloud services? The recent introduction of hardened industrial servers for IoT may indicate that server makers are looking for growth in vertical markets.

There are very compelling reasons for moving enterprise workloads to Amazon, Google, IBM and other hosted infrastructures. The scalability of on-demand resources, operating efficiency at cloud-scale and security are just three of many reasons. For instance, Google has 90 engineers working on just security where most enterprises are understaffed.

Last quarter, every enterprise server company posted declining revenues except for Dell. The server business is growing but not in the enterprise segment. The cloud companies do not purchase much from them. Instead, they purchase components built to their specification and build their infrastructure optimized for their enormous 24X7 workloads. Competitors — Google, Facebook, IBM and other cloud companies — collaborate on engineering and specifying new hardware components through the Open Compute Project founded by Facebook. The cloud companies are buying directly from the server makers supply chain. Declines during previous quarters demonstrate that this is a difficult-to-reverse, long-term trend.


The enterprise machine learning market is still young, but these servers will deliver high margins. Delivering powerful servers with GPUs optimized for the machine learning workloads of enterprise innovators will be profitable. Being early is important to acquiring market share as the sector matures.

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