ai hardware

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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.

Overview of AI hardware benchmarks: what they measure; how to read throughput, latency, and energy metrics across training and inference; and how batch size, precision, memory bandwidth, and software stacks skew results. It contrasts data‑center and edge devices, flags pitfalls of synthetic tests, and offers tips for selecting hardware by model, budget, and deployment constraints.