AI Chips are specialized processors optimized for the massively parallel linear-algebra workloads of machine-learning training and inference, including GPUs, TPUs, NPUs, and custom ASICs. They deliver high throughput on matrix and tensor operations through wide SIMD execution, high-bandwidth memory, and reduced-precision arithmetic. Control over advanced AI-chip design and fabrication has become a strategic axis of geopolitical and commercial competition.
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- Designs trade off training versus inference, precision (FP16, BF16, FP8, INT8), and memory bandwidth, with high-bandwidth memory and fast interconnects often the limiting factors at scale. Access depends on a concentrated fabrication supply chain, so export controls and foundry capacity directly shape who can train frontier models.