AI Accelerator
Specialized hardware designed to speed up AI and machine learning workloads, including GPUs, TPUs, and custom AI chips.
Why Specialized Hardware?
AI models perform massive amounts of matrix multiplication and parallel computation. General-purpose CPUs are not optimized for this. AI accelerators provide hundreds or thousands of cores optimized for parallel math operations.
Types of AI Accelerators
GPUs (NVIDIA): The dominant platform. CUDA ecosystem, H100/B100 chips. TPUs (Google): Tensor Processing Units custom-built for TensorFlow and JAX workloads. Custom ASICs: Amazon Trainium/Inferentia, Intel Gaudi, Cerebras wafer-scale chips. FPGAs: Field-programmable chips offering flexibility between custom silicon and software.
The Compute Arms Race
Training frontier AI models requires billions of dollars in compute. The competition for AI accelerator supply has become a geopolitical issue, with export controls on advanced chips reshaping the global AI landscape.