What It Is
Inference silicon covers processors and accelerators designed to run trained models efficiently at data-center, edge and device scale. The emphasis is energy per token or per inference, not only peak training throughput.
Why It Matters
As models move from training clusters into products and services, inference cost, latency and power become first-order design constraints. Architectures that can serve models efficiently shape which systems can be deployed at scale.
What to Watch
Watch how inference workloads split between data-center accelerators and edge SoCs, and which purpose-built architectures are used in deployed AI systems.