An AI chip is a semiconductor device engineered specifically to accelerate the mathematical operations that underpin machine learning—primarily large matrix multiplications, convolutions, and memory-bandwidth-heavy data transfers. General-purpose CPUs handle these workloads inefficiently; AI chips achieve orders-of-magnitude higher throughput by packing thousands of small arithmetic units that execute in parallel, combined with high-bandwidth memory architectures optimized for tensor operations. Major categories include NVIDIA GPUs (H100, B200), Google TPUs, AMD Instinct cards, and purpose-built inference chips such as AWS Inferentia, Groq's LPU, and Cerebras's wafer-scale engine.

Think of it like the difference between a Swiss Army knife and a specialized chef's knife. A CPU is versatile but slow at any single job; an AI chip does far fewer things but executes the one job that matters for neural networks—multiply-accumulate operations—at extreme speed and efficiency.

The hardware bottleneck matters directly for practitioners: a model that fits on a single H100's 80 GB of HBM3 memory can run Inference in seconds; a model that does not fit must be sharded across multiple chips, increasing cost and latency. Quantization techniques reduce memory requirements precisely to make larger models fit on fewer chips.

AI chips interact closely with Inference (the deployment-time workload they accelerate), Quantization (which reduces the precision of weights to fit more model on-chip), Pretraining (training runs consume millions of chip-hours), and Mixture of Experts architectures (which allow larger effective model sizes without proportional compute). Supply constraints on leading-edge chips have become a significant factor in the competitive landscape around Foundation Models.

You will encounter AI chip details in cloud provider pricing pages, model training cost analyses, hardware benchmark reports, and in the infrastructure sections of large-scale model training papers.