Hardware Accelerator
Specialized chips (e.g., GPUs, TPUs) designed to speed up AI computations, with implications for energy use and supply chain risk.
Purpose-built silicon (GPUs, TPUs, FPGAs, neuromorphic chips) optimized for matrix math and parallel workloads. Accelerators dramatically cut training and inference times but concentrate procurement risk (single-vendor lock-in), energy consumption, and e-waste concerns. Governance of accelerators covers vendor diversification, sustainability KPIs (performance-per-watt), end-of-life recycling programs, and secure firmware updates to guard against hardware-level attacks.
A cloud provider benchmarks NVIDIA GPUs against AMD Instinct accelerators for its AI-training clusters. They adopt a hybrid procurement strategy - mixing both vendors - to avoid single-source risk, deploy dynamic workload schedulers that favor the more energy-efficient devices during peak power-cost hours, and partner with an e-waste recycler to responsibly retire outdated cards.
“What used to take weeks of manual reviews and policy work is now structured and auditable in Enzai within minutes. It’s the first time AI governance has felt operational, not theoretical.”
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