Deploying Local LLMs: vLLM, Quantization, and Inference

Running Large Language Models (LLMs) locally or in production can seem daunting due to massive hardware requirements and complex configurations. As AI continues to evolve, the ability to host your own models efficiently is becoming an essential skill for developers and operations teams. This course breaks down the process of deploying and optimizing LLMs, transforming you from a beginner into someone capable of serving high-performance AI models efficiently. You will explore how to reduce memory footprints and maximize inference speed using modern techniques, ensuring you can run powerful models even with limited computational resources. What you'll learn: • Understand the foundational concepts of LLM architecture, inference, and memory management. • Calculate hardware requirements and estimate GPU VRAM needs for various model sizes. • Apply modern quantization methods like GGUF, AWQ, and GPTQ to optimize model weights. • Configure and deploy models using vLLM for high-throughput, low-latency inference. • Create standard REST API endpoints to seamlessly integrate local models into your applications. • Practice containerizing your LLM deployments using Docker for consistent, scalable environments. The journey begins with essential AI terminology and hardware basics before moving into hands-on written exercises focused on quantization and deployment. You will progress step-by-step through configuration scripts and deployment patterns used in modern MLOps. Designed for software developers, aspiring DevOps engineers, and tech enthusiasts with no prior machine learning experience, this text-based guide requires only a basic understanding of programming concepts. Start reading today to build your skills in modern AI deployment and inference optimization.