LLM Inference Infrastructure: Cost and Latency Optimization

Deploying large language models in production requires a deep understanding of the underlying hardware and the financial trade-offs involved. Without clear insights into latency and infrastructure costs, scaling your AI applications can quickly become unsustainably expensive. This text-only course guides you through the foundational concepts of LLM inference infrastructure, helping you make informed decisions about hardware selection, cost modeling, and latency optimization. You will learn how to analyze key performance metrics and choose the right deployment strategy for your business. What you'll learn: - Understand key latency metrics including Time to First Token (TTFT) and Tokens Per Second (TPS). - Analyze the economics of API-based models versus self-hosted open-source models on cloud infrastructure. - Evaluate hardware options including GPUs, TPUs, and specialized AI accelerators for inference workloads. - Explore modern optimization techniques such as model quantization, speculative decoding, and continuous batching. - Calculate the total cost of ownership (TCO) for hosting LLMs at various scales. - Practice designing cost-efficient and low-latency infrastructure architectures through written scenarios. You will begin with core terminology and the mechanics of LLM generation before moving into hardware comparisons and rigorous financial analysis. Through structured written examples and case studies, you will learn to calculate real-world hosting costs and design optimal serving strategies. This course is designed for software engineers, product managers, and technology leaders who are new to LLM infrastructure and want to understand the economic and technical factors of deployment. No prior hardware engineering experience is required. Start reading today to build cost-effective, high-performing AI infrastructure.