Large Language Model-Based Solutions

5Infrastructure and Deployment Tuning Strategies

WHAT'S IN THIS CHAPTER?

• Introduction to Tuning Strategies
• Hardware Utilization and Batch Tuning
• Inference Acceleration Tools
• Monitoring and Observability

INTRODUCTION TO TUNING STRATEGIES

The development and optimization of neural network models require not only algorithmic expertise but also an understanding of the underlying hardware and deployment infrastructure. The growth of computational resources and the advent of parallel processing have catalyzed advancements in deep learning, but they also present new challenges in the efficient utilization of these resources. In this chapter, we delve into strategies for infrastructure and deployment tuning, focusing on maximizing hardware utilization, accelerating inference, and employing monitoring and optimization to ensure sustained performance.

We first return to the basics and explain why it is so difficult to tune hardware utilization for LLMs. Then, we address the intricacies of training and inference, highlighting the need for strategic batch processing during training to fully exploit computational power while preserving model generalization. We also examine the critical role of inference acceleration in deployment, where the emphasis is on achieving cost‐effective and high‐performance models suitable for diverse application requirements. Integrating these strategies is essential for the successful deployment of deep learning models, ensuring they are both effective ...