Introduction

WHAT'S IN THIS CHAPTER?

• GenAI Applications and Large Language Models
• Importance of Cost Optimization
• Micro Case Studies
• Who Is This Book For?

GenAI APPLICATIONS AND LARGE LANGUAGE MODELS

Large language models (LLMs) have evolved to become a cornerstone in the domain of text-based content generation. They can produce coherent and contextually relevant text for a variety of applications, making them invaluable assets in today's digital landscape. One notable example is OpenAI's GPT-4, which reportedly ranked in the 90th percentile of human test takers on the Uniform BAR Examination, showcasing its advanced language understanding and generation capabilities?. Generative AI tools (like ChatGPT, for example) may use LLMs, but also other kinds of large models (e.g., foundational vision models). These models serve as the backbone for many modern applications, facilitating a multitude of tasks that would otherwise require substantial human effort for building bespoke, application-specific models. The capabilities of these models to understand, interpret, and generate human-like text are not only pushing the boundaries of what's achievable with AI but also unlocking new avenues for innovation across different sectors. To reemphasize what's already obvious, Figure 1 shows Google Trends interest over time for the term Generative AI worldwide.

FIGURE 1: Google Trends chart of interest over time for the term Generative AI worldwide

Generative AI (GenAI) and LLMs represent two interlinked domains within artificial intelligence, both focusing on content generation but from slightly different angles. GenAI encompasses a broader category of AI technologies aimed at creating original content. While LLMs excel at text processing and production, GenAI places a broader emphasis on creativity and content generation across different mediums. Understanding the distinctions and potential synergies between these two areas is crucial to fully harness the benefits of AI in various applications, ranging from automated customer service and content creation to more complex tasks such as code generation and debugging?. This field has seen rapid advancements, enabling enterprises to automate intelligence across multiple domains and significantly accelerate innovation in AI development??. On the other hand, LLMs, being a subset of GenAI, are specialized in processing and generating text. They have demonstrated remarkable capabilities, notably in natural language processing tasks and beyond, with a substantial influx of research contributions propelling their success?.

The proliferation of LLMs and GenAI applications has been fueled by both competitive advancements and collaborative efforts within the AI community, with various stakeholders including tech giants, academic institutions, and individual researchers contributing to the rapid progress witnessed in recent years?. In the following sections, we will talk about the importance of cost optimization in this era of LLMs, explore a few case studies of successful companies in this area, and describe the scope of the rest of the book.

IMPORTANCE OF COST OPTIMIZATION

The importance of cost optimization in the development and operation of GenAI applications and LLMs cannot be understated. Cost can ultimately make or break the progress toward a company's adoption of GenAI. This necessity stems from various aspects of these technologically advanced models. GenAI and LLMs are resource-intensive by nature, necessitating substantial computational resources to perform complex tasks. Training state-of-the-art LLMs such as OpenAI's GPT-3 can involve weeks or even months of high-performance computing. This extensive computational demand translates into increased costs for organizations leveraging cloud infrastructure and operating models.

The financial burden of developing GenAI models is considerable. For instance, McKinsey estimates that developing a single generative AI model costs up to $200 million, with up to $10 million required to customize an existing model with internal data and up to $2 million needed for deployment. Moreover, the cost per token generated during inference for newer models like GPT-4 is estimated to be 30 times more than that of GPT-3.5, showing a trend of rising costs with advancements in model capabilities. The daily operational cost for running large models like ChatGPT is significant as well, with OpenAI reported to spend $700,000 daily to maintain the model's operations.

GenAI models require high utilization of specialized hardware like graphics processing units (GPUs) and tensor processing units (TPUs) to accelerate model training and inference. These specialized hardware units come at a premium cost in cloud infrastructure, further driving up the expenses. Companies trying to do this on-premises, without the help of cloud providers, may need a significant, up-front capital investment.

Beyond compute requirements, large-scale, high-performance data storage is imperative for training and fine-tuning GenAI models, with the storage and management of extensive datasets incurring additional cloud storage costs. As AI models evolve and adapt to ever-increasing stores of data (like the Internet), ongoing storage requirements further contribute to overall expenses. This is why scalability poses a significant challenge in cost optimization. Rapid scaling to accommodate the resource demands of GenAI applications can lead to cost inefficiencies if not managed effectively. Overscaling can result in underutilized resources and unnecessary expenditure, whereas underscaling may hinder model performance and productivity.

Strategies to optimize costs while scaling GenAI in large organizations include prioritizing education across all teams, creating spaces for innovation, and reviewing internal processes to adapt for faster innovation where possible.

Pre-training a large language model to perform fundamental tasks serves as a foundation for an AI system, which can then be fine-tuned at a lower cost to perform a wide range of specific tasks. This approach aids in cost optimization while retaining model effectiveness for specific tasks.

Conducting a thorough cost-value assessment to rank and prioritize GenAI implementations based on potential impact, cost, and complexity can lead to better financial management and realization of ROI in GenAI initiatives. Lastly, the most common pattern seen today is for "model providers" to spend and try to recoup their costs by providing an API and for "model consumers" to heavily optimize their costs by using GenAI model APIs without the need for any up-front investment or even data.

Challenges and Opportunities

The pathway to cost optimization in GenAI applications with large language models is laden with both challenges and opportunities. These arise from the inherent complexities of the models and the evolving landscape of AI technologies. The following are the principal challenges and the accompanying opportunities in this domain:

• Computational demands: LLMs like GPT-3 or BERT require substantial computational resources for training and inference. The high computational demands translate to increased operational costs and energy consumption, which may create barriers, especially for small to medium-sized enterprises (SMEs) with limited resources.

  Opportunity: The challenge of computational demands opens the door for innovation in developing more efficient algorithms, hardware accelerators, and cloud-based solutions that can reduce the cost and energy footprint of operating LLMs.

• Model complexity: The complexity of LLMs, both in terms of architecture and the amount of training data required, presents challenges in achieving cost optimization. The model's size often correlates with its performance, with larger models generally delivering better results at the expense of increased costs.

  Opportunity: This challenge catalyzes the exploration and adoption of techniques such as model pruning, quantization, and knowledge distillation that aim to reduce model size while retaining or even enhancing performance.

• Data privacy and security: Handling sensitive data securely is a paramount concern, especially in sectors such as healthcare and finance. The cost of ensuring data privacy and security while training and deploying LLMs can be significant.

  Opportunity: The necessity for robust data privacy and security solutions fosters innovation in privacy-preserving techniques, such as federated learning, differential privacy, and encrypted computation.

• Scalability: Scaling GenAI applications to accommodate growing data and user demands without a proportional increase in costs is a formidable challenge.

  Opportunity: This challenge drives the advancement of scalable architectures and technologies that allow for efficient scaling, such as microservices, container orchestration, and serverless computing.

• Model generalizability and domain adaptation: Achieving high performance on domain-specific tasks often requires fine-tuning LLMs with additional data, which can be cost-intensive.

  Opportunity: This...