Why AI Infrastructure Sizing Is Different
AI infrastructure sizing is different from traditional server and virtualisation design. Workloads are less predictable, user demand varies and GPU specifications only tell part of the story. A better approach combines theoretical sizing with practical testing, measurement and POCs to build the evidence needed for confident production infrastructure decisions.
AI Infrastructure Guide for the Mid-Market
A practical introductory guide to planning on-premises AI infrastructure for mid-market organisations. It covers the key considerations across workloads, GPUs, memory, storage, networking, power, cooling, security and operations, with a focus on building platforms that are appropriately sized, supportable and able to scale as AI requirements develop.
Unified Memory or Discrete GPU?
A practical comparison looking at cost, performance, scalability and redundancy for AI infrastructure.
Power Planning for Modern Servers: Why Yesterday’s Server Room May Not Support Tomorrow’s Workloads
Modern servers, GPU workloads and AI infrastructure are increasing power, UPS and cooling demands. Learn how to assess whether your server room is ready for tomorrow’s workloads.