Frontier artificial intelligence compute usually combines large clusters of current-generation accelerators with fast networking, storage and optimized software. The word frontier is relative because the performance threshold rises as hardware and model techniques improve.
Usable frontier capacity depends on more than the nominal number of chips. Hardware age, power efficiency, cluster reliability and the ability to keep accelerators supplied with data all affect the effective compute available to a laboratory.
Acronyms and aliases
frontier AI compute acronymfrontier compute variant
General terms
Related terms
Frequently asked questions
How is frontier artificial intelligence compute measured?
It can be described through accelerator count, numerical throughput, training operations, power use and the effective performance delivered by a complete cluster.
Why does newer hardware matter for frontier compute?
New accelerators can deliver more useful model work per unit of power and space than older installed hardware, increasing effective capacity.
