What is GPU Acceleration

Definition

GPU acceleration is the use of graphics processing units to perform the massively parallel computations that AI training and inference require, dramatically speeding up workloads compared with CPUs by processing many operations simultaneously.

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Speeds AI training and inference through massive parallelism
Makes training large models practical in feasible timeframes
Improves throughput and lowers latency for inference at scale
Underpins the compute behind modern deep learning

Real World Example

A research team trains a deep neural network on GPUs, completing in hours what would take weeks on CPUs, because the GPUs process the many matrix operations of training in parallel.

FAQs

Why are GPUs used for AI?

Their massively parallel architecture suits the matrix and tensor operations that dominate AI training and inference, far outpacing CPUs.

What workloads benefit most?

Training and serving deep neural networks, including large language and vision models, benefit greatly from GPUs.

Are GPUs the only AI accelerators?

No, specialised chips like TPUs and other accelerators also speed AI workloads, but GPUs are the most widely used.