Abstract
What actually happens when you send a prompt to an LLM? What are tokens, tensors, attention, quantization, and all those mysterious gigabytes of VRAM doing? And why does your GPU suddenly become the most important piece of hardware in the room? This talk takes a developer-friendly tour through the fundamentals of modern LLMs. We’ll try to demystify concepts like context windows, model quantization, and inference performance without requiring a PhD in linear algebra. Then we’ll take those concepts out of the textbook and into the real world, looking at tools such as LM Studio and what it takes to run useful models on your own hardware. By the end, you’ll have a mental model for what an LLM is actually doing, how local inference works, and why sometimes the answer to “How much VRAM do I need?” is apparently “all of it.”
Alan P. Barber
Software Developer, Computer Scientist, Scrum Master, & Crohn’s Disease Fighter
I specialize in Software Development with a focus on Architecture and Design.