AI Vocabulary · 02 AI Infrastructure
What is AI Chip?
A processor built for the calculations AI needs, such as a GPU or a custom chip, trading flexibility for speed and efficiency.
AI chips, also called AI accelerators, are processors specialised for the calculations of AI. They include GPUs and chips designed for one purpose, so-called ASICs, such as Google's TPUs or Amazon's Trainium. They give up efficiency at other tasks in exchange for high efficiency at AI.
The difference to a normal CPU is large. In 2020, the research centre CSET estimated AI chips to be roughly 10 to 1,000 times more efficient and faster than CPUs for training and running AI models, depending on the chip and the task.
A few designers supply most of them. Epoch AI tracks four: NVIDIA, AMD, Google and Amazon. Together they used about 90 percent of the world's high-bandwidth memory (HBM) in 2025.
Why it matters for you
Whoever designs, builds and owns the chips shapes the AI market. Chip supply decides how fast new models arrive, how much inference costs and which providers can serve you reliably.
Own it
You will not buy AI chips in bulk, but you choose providers who depend on them. Ask where your workloads run, and keep your setup able to switch between providers and models.
Where it comes from
The definition and the 2020 efficiency estimates come from AI Chips: What They Are and Why They Matter by Saif M. Khan and Alexander Mann (CSET, April 2020). The figures on designers come from Epoch AI's AI chip components methodology (2026). Both are estimates.
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