GPU (Graphics Processing Unit)
A specialised processor that accelerates parallel computations for training and running AI models.
GPU (Graphics Processing Unit)
A specialised processor designed to accelerate complex computations, widely used for training and running machine learning models.
What Is a GPU
A GPU is a parallel computing processor originally developed for graphics rendering. Unlike traditional processors that optimise for sequential tasks, GPUs execute many operations at once, making them highly effective for matrix operations common in AI workloads.
Why GPUs Are Important for AI
AI training and inference involve billions of mathematical operations. GPUs reduce runtime significantly by handling these operations in parallel, enabling faster experimentation, larger model architectures, and more efficient deployment across translation and language technology pipelines.
GPUs vs CPUs in Machine Learning
CPUs offer flexibility for general-purpose computing and low-latency control tasks, while GPUs provide much higher throughput for tensor-heavy computations. In machine learning, CPUs often manage orchestration and preprocessing, while GPUs perform core model training and large-scale inference.
Role of GPUs in Training Large Language Models
Training large language models requires high-bandwidth memory, distributed compute, and sustained numerical throughput. Multi-GPU clusters enable parallel training strategies that make modern LLM development feasible and reduce training time from months to manageable production cycles.
Applications in AI Infrastructure
GPUs are foundational across AI infrastructure, including model training platforms, inference APIs, real-time translation engines, and retrieval-augmented systems. Their performance characteristics directly influence scalability, cost, and response quality in enterprise AI services.