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.
Related Terms
GDPR (General Data Protection Regulation)
EU legislation regulating the processing and protection of personal data.
Gender Bias in AI
Differences in how AI treats or represents genders due to training-data patterns.
Generative AI
AI systems that generate new text, images, audio, code, or other content from learned patterns.
Gradient Descent
An optimisation algorithm that iteratively updates model parameters to reduce prediction error.
Glossary-driven Translation
Translation guided by predefined terminology lists.
Related Resources
Attention Mechanism
A neural network method that helps AI models focus on the most relevant parts of the input when generating output, improving context handling and translation quality.
Multimodal AI Models
AI systems that combine text, image, audio, and video inputs to improve understanding and generation across tasks.
OOV (Out-of-Vocabulary)
Words or tokens that do not appear in a model’s training vocabulary and therefore cannot be directly recognised or translated by the system.
Speech Recognition
Technology that converts spoken language into written text using machine learning models.
Tokenisation in Natural Language Processing
How text is segmented into machine-readable units for NLP pipelines and large language models.
Training Large Language Models
How LLMs are pretrained, optimised, and adapted using large datasets and distributed compute.