Hardware Design for LLM Inference: Von Neumann Bottleneck

Dec 30, 2023 ·

LLM System Design is a comprehensive guide to designing large language models (LLMs) for natural language processing. This guide offers an in-depth look at the components of LLMs, how they work, and how to design them from top to bottom. It covers topics such as architecture, subword modeling, data preprocessing, model training, hyperparameter optimization, performance metrics, and more. The goal of this guide is to equip developers and researchers with the tools necessary to build their own powerful LLM systems.

The guide begins by discussing the basics of LLM architecture, including core components, algorithm selection, and hyperparameter optimization. Next, it dives into the details of subword modeling, which includes two categories: tokenization and embedding. Tokenization takes raw text and breaks it up into individual words or phrases, while embedding turns those tokens into numerical vectors that can be used for machine learning. After subword modeling, the guide delves into data preprocessing, which includes steps like cleaning, normalizing, and balancing datasets. Finally, it covers the fundamentals of model training, including loss functions, optimization strategies, and regularization techniques.

Once the model has been trained, the guide discusses various performance metrics that can be used to evaluate its accuracy. These include precision, recall, and F1 score. Additionally, the guide provides tips on how to optimize a model’s performance, such as hyperparameter tuning and ensembling. It also covers deployment methods, such as REST APIs and containers.

Overall, LLM System Design provides a comprehensive overview of the process of designing and building a large language model. It gives readers a firm understanding of the components of LLMs, the data preprocessing and training steps needed for successful model development, and the performance metrics that can be used to evaluate models. With this guide, developers and researchers have the knowledge they need to build their own powerful LLM systems.