
Data centers span from single-rack edge sites to multi-building campuses. Here’s how to choose the right scale for your workload, power, and growth profile.
Data centers vary enormously in physical size, ranging from edge sites with just a few servers to sprawling multi-building campuses spanning many football fields. The decision about how large to build a data center matters because overbuilding wastes capital and creates unnecessary operating costs, while underbuilding can hamper efficiency and future growth. Right-sizing requires matching a facility's physical footprint to actual workload needs by calculating compute requirements, choosing appropriate server types and quantities, designing rack density and cooling systems, selecting materials wisely, and planning for phased expansion over time. The goal is alignment between physical scale and both current capacity needs and a clear plan for how to scale as demand changes.

In this tutorial, we explore TileLang as a high-level Python domain-specific language for designing and compiling performance-oriented GPU kernels through TVM. We begin by validating the CUDA environment and establishing reusable benchmarking and numerical-verification utilities, then progressively implement vector addition, tiled tensor-core matrix multiplication, schedule exploration, fused GEMM epilogues, row-wise softmax, and FlashAttention. Throughout the tutorial, we work directly with Ti

A close call in Northern Virginia revealed just how poorly data centers respond to grid disruptions. Here's how to fix the problem.

As AI infrastructure grows more complex, companies are rethinking how they acquire, own, and finance assets that operate on dramatically different economic timelines.
Want to go deeper than the news? Explore live, cohort-based AI courses taught by practitioners.
Browse AI courses on Maven