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more energy-efficient devices during peak power-cost hours, and partner with an e-waste recycler to responsibly retire outdated cards.
We help you find answers
What problem does Enzai solve?
Enzai provides enterprise-grade infrastructure to manage AI risk and compliance. It creates a centralized system of record where AI systems, models, datasets, and governance decisions are documented, assessed, and auditable.
Who is Enzai built for?
How is Enzai different from other governance tools?
Can we start if we have no existing AI governance process?
Does AI governance slow down innovation?
How does Enzai stay aligned with evolving AI regulations?
Research, insights, and updates
Empower your organization to adopt, govern, and monitor AI with enterprise-grade confidence. Built for regulated organizations operating at scale.