LOCAL AI ON YOUR PC: How to Run Private AI, LLMs, Coding Agents and Generative AI on Consumer Hardware (Precision Engineering Book 11)
English | October 9, 2026 | ASIN: B0HMK1647L | 277 pages | Epub | 483.68 KB
What if your PC could become your private AI workstation-without sending your sensitive data to someone else's servers?Are you tired of cloud AI services handling your documents, source code, prompts, or personal information? Have you tried running an LLM locally only to discover that VRAM, RAM, quantization, model size, and performance are more complicated than the tutorials suggested?Do you want a local coding assistant or AI agent, but worry about giving an AI system access to your files, terminal, or development environment? Or are you frustrated by guides that show you how to launch a model but never explain why it fails, slows down, runs out of memory, or becomes difficult to maintain?Local AI on Your PC takes a different approach.Instead of focusing on copy-and-paste commands, this book teaches you how to design, build, secure, evaluate, troubleshoot, and maintain practical AI systems on consumer hardware.You'll learn how to move from experimentation to engineering:From guessing whether a model will fit to sizing hardware and memory requirements intelligentlyFrom downloading random models to choosing models, runtimes, formats, and quantization levels deliberatelyFrom basic chat to private RAG assistants over your documentsFrom AI-generated snippets to repository-aware coding assistantsFrom prompts to safer autonomous coding agents with controlled tool accessFrom mysterious failures to systematic benchmarking and troubleshootingFrom isolated tools to integrated workflows using APIs, applications, files, databases, Git, and development toolsInside, you'll discover how to work with local LLMs, coding agents, image generation, speech, audio, video, automation, and multimodal workflows while understanding the hardware and software constraints behind them.You'll learn how VRAM, RAM, context windows, KV cache, quantization, GPU acceleration, storage, latency, throughput, and thermal behavior affect real workloads. You'll also learn how to protect credentials and private data, isolate network services, restrict agent capabilities, validate AI-generated results, and build systems that remain dependable as models and runtimes change.Whether you're a beginner tired of incomplete tutorials, a developer building private AI tools, a privacy-conscious user, a PC enthusiast optimizing existing hardware, or an AI early adopter, this book gives you a practical engineering framework for making better decisions.Because running a model is only the beginning.The real goal is to build local AI you can understand, control, troubleshoot, and depend on.If you're ready to move beyond fragile experiments and build a capable private AI environment on hardware you control, this is the guide that will help you make local AI work-and keep working.
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