AI Accelerator

An AI accelerator is specialised hardware built specifically to handle the mathematical operations that AI models require. While general-purpose CPUs can run AI workloads, they are not designed for the massive parallel computations that training and inference demand. AI accelerators are.

Why specialised hardware matters

AI workloads are fundamentally different from traditional computing. Training a large language model involves trillions of matrix multiplications — simple arithmetic operations repeated at enormous scale. General-purpose CPUs execute instructions one at a time (or a few at a time). AI accelerators process thousands of operations simultaneously.

The result is dramatic: an AI accelerator can perform in hours what a CPU would take weeks to accomplish.

Types of AI accelerators

• GPUs (Graphics Processing Units): Originally designed for rendering graphics, GPUs excel at parallel computation. NVIDIA dominates this market with their A100 and H100 chips. GPUs are the workhorses of modern AI.
• TPUs (Tensor Processing Units): Google's custom-designed chips optimised specifically for neural network operations. Available through Google Cloud.
• NPUs (Neural Processing Units): Low-power AI chips built into smartphones and laptops for on-device inference. Apple's Neural Engine and Qualcomm's AI Engine are examples.
• FPGAs (Field-Programmable Gate Arrays): Reconfigurable chips that can be customised for specific AI workloads. Used in latency-sensitive applications.
• ASICs: Fully custom chips designed for a single purpose. Tesla's Dojo chip for autonomous driving training is an example.

The GPU shortage

Demand for AI accelerators — especially NVIDIA's high-end GPUs — has far exceeded supply since the AI boom began. This has driven up prices, created waiting lists, and made AI compute access a strategic concern for organisations of all sizes.

Cloud vs on-premise

Most organisations access AI accelerators through cloud providers (AWS, Google Cloud, Azure) rather than purchasing hardware. This provides flexibility and avoids large upfront investments. However, for sustained high-volume workloads, on-premise or reserved cloud capacity can be more cost-effective.

The impact on AI strategy

AI accelerator availability and cost directly influence what AI projects are feasible. Understanding the hardware landscape helps you plan AI initiatives, budget for compute costs, and evaluate whether to use cloud AI services or invest in your own infrastructure.