Generative AI Inference Optimization: Speed Up GenAI Models

Deploying generative AI models can be slow and expensive, often leading to high latency and massive compute costs. Understanding how to optimize these models for production is essential for building responsive, cost-effective AI applications. This text-based course guides you through the core principles of inference optimization. You will move from understanding basic model bottlenecks to applying practical techniques that make generative AI systems run faster and consume fewer resources. What you'll learn: Understand the foundational concepts of model inference and where latency bottlenecks occur; Apply quantization techniques to reduce model size and speed up computation without losing accuracy; Implement model pruning and distillation to remove redundant parameters from your networks; Configure KV caching and batching strategies to handle multiple user requests efficiently; Analyze modern hardware considerations and memory management for large language models. You will begin by learning key terminology and the mechanics of model execution before diving into written explanations and code snippets for each optimization method. This course is designed for software developers, aspiring AI engineers, and tech enthusiasts who want to learn the fundamentals of model optimization without needing complex prerequisites. Start reading today to make your generative AI systems faster and more efficient.