Enterprise IT Insights, Product Reviews & Strategy!
A server refresh cycle is not simply a countdown to replacing old hardware. It is a structured process for deciding whether each server should be upgraded, replaced, redeployed, sold, or securely retired. Age matters, but it should not control the
AI data center networking is now a direct driver of AI performance. In a traditional data center, the network mainly supports application traffic, storage access, and routine east-west communication. In an AI cluster, the network affects how quickly GPUs can
AI demand is creating a broader server memory cycle, not just a shortage of high-bandwidth memory (HBM). As GPU clusters grow, memory suppliers must support more HBM for accelerators while also supplying DDR5 and registered DIMMs (RDIMMs) for CPU-based servers.
Artificial intelligence is pushing data center infrastructure beyond traditional limits. Modern AI workloads rely on dense clusters of GPUs that generate intense heat in a confined space, far exceeding what conventional systems were designed to handle. Air cooling, once the
Cloud migration has become a strategic priority for enterprises that need to improve agility, modernize operations, and support long-term growth. It is no longer limited to moving servers or applications from one environment to another. A well-defined cloud migration strategy
A practical move from a legacy data center to an AI-ready hybrid environment starts with the workload, data, facility, and operating limits not with a GPU purchase. First, assess the use case, data path, compute, storage, network, power, cooling, security,
Modern IT environments no longer operate within a single, defined network. Users connect from home, offices, and mobile devices. Applications run across cloud platforms, on-prem systems, and hybrid environments. Data moves between all of them constantly. This shift has made
AI infrastructure has changed quickly. What was once built around general-purpose GPU clusters is now shifting toward tightly integrated platforms designed for large models, high-throughput inference, and demanding HPC workloads. The move from NVIDIA A100 to H100 reflects that shift
Data centers run the systems that support cloud platforms, AI workloads, storage, networking, and enterprise applications. But as demand grows, data centers face a difficult challenge: they must expand capacity while reducing waste, energy use, and hardware costs. Data center
No, most AI teams do not automatically need the newest GPU generation. The right GPU depends on the workload: model size, precision, memory capacity, memory bandwidth, latency, throughput, concurrency, software support, power limits, and how many GPUs must work together.
AI training and AI inference use many of the same GPUs, but they place very different demands on infrastructure. Training builds or updates a model. Inference uses that trained model to answer requests, generate content, classify data, or support applications
Choosing an E-Rate vendor is one of the most important decisions in a school or library network project. The lowest price may look attractive, but it may not always include the right hardware, support, licenses, accessories, or delivery timeline. A
