FPGA-Accelerated Large Language Models Used for ChatGPT

By: Bill Jenkins – Director of AI/ML

Introduction: Large Language Models

In recent years, large language models (LLMs) have revolutionized the field of natural language processing, enabling machines to generate human-like text and engage in meaningful conversations. These models, such as OpenAI's GPT, possess an astounding ability to comprehend and produce language. They can be used for a wide range of natural language processing tasks, including text generation, translation, summarization, sentiment analysis, and more.

Large language models are typically built using deep learning techniques, particularly using transformer architectures. Transformers are neural network models that excel at capturing long-range dependencies in sequences, making them well-suited for language understanding and generation tasks. Training a large language model involves exposing the model to massive amounts of text data, often from sources such as books, websites, and other textual resources. The model learns to predict the next word in a sentence or fill in missing words based on the context it has seen. Through this process, it gains knowledge about grammar, syntax, and even some level of world knowledge.

One of the primary challenges associated with large language models is their immense computational and memory requirements. These models consist of billions of parameters, necessitating powerful hardware and significant computational resources to train and deploy them effectively as discussed in Nishant Thakur's March 2023 LinkedIn article, "The Mind-Boggling Processing Power and Cost Behind ChatGPT: What It Takes to Build the Ultimate AI Chatbot?". Organizations and researchers with limited resources often face hurdles in harnessing the full potential of these models due to the vast array of processing needed or money for the cloud. In addition, extreme growth in the context lengths that need to be stored to create the appropriate tokens, words or sub-parts of words, when generating responses puts even more demands on memory and compute resources.

These compute challenges lead to higher latency which makes the adoption of LLMs that much more difficult and not real-time and, therefore, less natural. In this blog, we will delve into the difficulties encountered with large language models and explore potential solutions that can pave the way for their enhanced usability and reliability.

Explore how FPGA-accelerated language models reshape generative AI, with faster inference, lower latency, and improved language understanding: https://bit.ly/3OFhXn5