GPU Computing

GPU computing leverages graphics processing units for general-purpose computation, particularly AI and machine learning workloads. GPUs are fundamentally parallel processors — while a CPU might have 8-64 powerful cores, a modern GPU has thousands of simpler cores that can perform many calculations simultaneously. This massive parallelism makes GPUs ideal for the matrix operations that dominate AI workloads. Training a neural network involves billions of matrix multiplications, and a GPU can perform thousands of these simultaneously. Tasks that would take weeks on CPUs can be completed in hours or days on GPUs. NVIDIA dominates the AI GPU market with products like the A100, H100, and B200 series. AMD offers alternatives with its MI series, and cloud providers are developing custom AI accelerators (Google TPUs, AWS Trainium/Inferentia). The choice of hardware significantly impacts performance, cost, and software compatibility.

GPU availability and cost are fundamental constraints on AI operations. The cost of GPU compute determines the economics of model training, fine-tuning, and inference. GPU shortages can delay AI projects and inflate costs. Businesses must decide between purchasing GPUs (high capital expense, maximum control), leasing through cloud providers (flexible, pay-per-use), or using managed AI services (highest abstraction, no GPU management). Each approach has different cost profiles, scaling characteristics, and operational requirements. Understanding GPU economics helps organisations make better decisions about model selection, inference optimisation, and build-versus-buy choices. A model that performs 5% better but requires 4x the GPU compute may not be the right choice. Cost-conscious GPU utilisation — through techniques like quantisation, batching, and model selection — directly impacts AI programme sustainability.