What is an artificial intelligence inference chip?
Definition
An artificial intelligence inference chip accelerates the operations used when a trained model produces predictions or generated output. Designs may optimize matrix arithmetic, low-precision computation, on-chip memory, dataflow, and model-serving concurrency.
The best chip depends on model architecture and workload. Hardware must be evaluated with software, memory, networking, batch size, latency targets, and real power use. Custom designs can improve efficiency but require major manufacturing, compiler, and integration investment.
Acronyms and aliases
AI inference chip variantinference accelerator variant
General terms
Related terms
Frequently asked questions
How is an inference chip different from a training chip?
Inference emphasizes serving completed models with predictable latency and efficiency, while training emphasizes large-scale forward and backward computation.
Why do model providers design custom inference chips?
They can tune arithmetic, memory, networking, and software for their workloads and reduce cost, latency, or dependence on external hardware suppliers.
