Running LLMs Locally on Your GPU: A Practical Guide

Running large language models locally gives you full privacy, complete control, and zero cloud API costs. This course guides you through configuring and operating modern open-weight language models directly on your own hardware. You will gain the practical skills needed to set up local inference runtimes, understand hardware requirements, manage memory constraints, and integrate local models into your personal development workflows. What you'll learn: - Understand core architecture fundamentals and essential GPU memory concepts - Configure modern local inference engines such as llama.cpp and Ollama - Apply quantization techniques like GGUF and AWQ to fit larger models into consumer VRAM - Manage context windows, prompt formatting, and sampling parameters effectively - Connect local models to developer tools and programmatic APIs - Practice troubleshooting common hardware bottlenecks and out-of-memory errors You will start with foundational terminology and hardware concepts before moving systematically into model selection, runtime configuration, and practical execution through structured text explanations and code examples. This course is designed for developers, data enthusiasts, and beginners looking to harness the power of open-source language models on their own machines without prior local AI deployment experience. Start reading today and take full control of your local AI workflow.