You have heard that running AI on your own computer is possible, but every headline leaves out the messy details. You encounter terms like VRAM, quantization, and tokens per second, and each new term opens another rabbit hole of conflicting advice. This book exists to flatten that learning curve for you, consolidating everything you need to know about building a local AI rig, from a budget build for under $1,000 to an enthusiast system that runs the largest open models.
This book is not written by someone who has only studied the theory. It is written by someone who has built, tested, and broken local AI rigs across every price point. It covers the fundamentals that stay relevant even as the specific models and tools change. You do not need to be a machine learning engineer to benefit. You need curiosity and a willingness to experiment.
Inside, you'll discover:
• Why VRAM is the single most important factor for local AI and how to calculate your needs
• How to shrink models without breaking them using quantization techniques like GGUF and GPTQ
• A complete survey of the GPU landscape: NVIDIA, AMD, and Apple Silicon
• How to build a budget rig for under $1,000 and an enthusiast system for $2,000-$4,000
• The software that makes hardware work, including Ollama, LM Studio, and vLLM
• How to serve models from a local playground to production for a team
• Prompt engineering for quantized hardware and fine-tuning on consumer GPUs
Start with what you have. Upgrade when you need more. This book gives you the framework to make informed decisions about your own AI rig.
