AI accelerator: what it means.
An accelerator speeds operations used by AI workloads through specialized or optimized hardware. It may be a separate device or an integrated processing feature. Intel describes CPU-integrated capabilities as well as discrete acceleration. (Intel: AI accelerators)
GPU, NPU, FPGA and other hardware names describe different implementations. Supported software, model operations and numerical formats determine whether a particular application can use the available acceleration. (Intel: AI accelerators)
Put the definition to work.
A GPU, NPU, or purpose-built accelerator can play this role. Read the supported workloads and software requirements rather than assuming every accelerator runs the same models. (Intel: AI accelerators)
Questions to ask next.
Use these questions as a reading checklist, not as a device-selection recommendation. Exact manufacturer documentation takes priority over a general definition.
- Supported models and operations
- Software runtime and numerical formats
- Workload-specific latency, throughput and power
For the broader explanation, compare ai processing roles. This page defines the term; the companion guide connects it to the rest of the system.
Common AI accelerator questions.
Does accelerator mean the whole AI application runs on that hardware?
Not necessarily. A system can divide work among a CPU and other engines. Identify which operations are accelerated and measure the complete application separately. (Intel: AI accelerators)