Generative AI – Large Pre-Trained Language Models

What are Large Pre-Trained Language Models?

Discover Large Pre-Trained Language Models (LPLMs), a subset of Generative AI models projected to soar by 34% CAGR, leading to a 110 billion USD market share by 2030. LPLMs are deep learning neural networks designed to learn from vast amounts of text or code data. The process starts with pre-training on general language tasks, such as predicting the next word or filling in blanks, which requires tremendous computational power. Once trained, LPLMs can automate tasks such as answering questions or summarizing texts.

Popular examples of LPLMs are BERT (Bidirectional Encoder Representations from Transformers), GPT-n (Generative Pre-trained Transformer), and T5 (Text-to-Text Transfer Transformer).

Why Large Pre-Trained Language Models are critical for AI workload?

Improved Performance: LPLMs, such as GPT-3 or BERT, have demonstrated remarkable performance in terms of accuracy and comprehension in various natural language processing (NLP) tasks.

Transfer Learning: LPLMs can be fine-tuned for specific tasks with relatively small amounts of labeled data, leveraging the knowledge gained during pre-training. This allows AI companies to develop customized models with accelerated development cycles.

Cost Savings: Leveraging LPLMs enables AI companies to reduce the costs associated with training models while still achieving high performance.

How GPU optimized systems are critical for Large Pre-Trained Language Models?

To efficiently train and deploy LPLMs, businesses must have a GPU-optimized infrastructure in place. LPLMs require enormous computational power including high bandwidth memory and fast communication among GPUs, to handle the demanding workload.