Deploying Large NLP Models: Infrastructure Cost Optimization

NLP models in commercial applications such as text generation systems have experienced great interest among the user. These models have achieved various groundbreaking results in many NLP tasks like question-answering, summarization, language translation, classification, paraphrasing, et cetera. Models like for example ChatGPT, Gopher **(280B), GPT-3 (175B), Jurassic-1 (178B), and Megatron-Turing NLG (530B) are predominantly very large and often addressed as large language models or LLMs. These models can easily have millions or up to billions of parameters making them financially expensive to deploy and maintain. Such large natural language processing models require significant computational power and memory, which is often the leading cause of high infrastructure costs. Even if you are fine-tuning an average-sized model for a large-scale application, you need to muster a huge amount of data.