This post was originally published on Data Center Knowledge
As companies pour resources into AI, it is important to understand the different types of data centers supporting AI development and their unique features. Data centers are critical pieces of digital infrastructure that provide the computing power for real-time analytics, AI solutions, and global communications. Yet not all data centers are the same – from edge to hyperscale data centers, each must design and plan for necessary energy consumption while balancing sustainability and cost-efficiency. Complications can arise when developers are buying land for new data centers, updating existing data center infrastructure, and navigating state and local government regulations.
Despite these challenges, investors are poised to take advantage. The global hyperscale data center market is expected to grow from $320.59B in 2023 to $1.44T in 2029. The edge data center market alone is projected to expand at a CAGR of nearly 10% each year until 2030. Knowing the differences between types of data centers will be key to capturing that growing value.
What Are Edge and Hyperscale Data Centers?
Edge and hyperscale data centers are distinguished by two main elements: physical proximity to the end-user and physical size of the data center. Edge data centers are located closer to the end-user and are often
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