accelerators

A practical, checklist-driven guide to AI hardware selection and deployment. It covers training vs. inference needs; GPU vs. specialized accelerators; memory bandwidth/capacity; interconnects and networking; storage; power and thermals; form factors; and software stacks. It also addresses observability, security, reliability, TCO, and offers guidance for edge vs. data center, benchmarking, scaling, and future-proofing.