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Running Local LLMs on Your Own Hardware is a structured, hands-on guide to running open-weight large language models on hardware you control. The content is organized around a durable mental model of local inference:

how models use memory and compute, how to read your hardware, how to choose models and quantization, how to operate the major inference engines, how to serve and integrate models, and how to benchmark, scale, secure, and troubleshoot a local deployment.

- Why and when to run LLMs locally; the open-weight model landscape

- Transformer inference internals relevant to hardware decisions

- Hardware selection and sizing; model and quantization selection

- Environment setup, inference engines, and first runs

- API serving, retrieval-augmented generation, and fine-tuning

- Benchmarking, scaling, security, privacy, and troubleshooting

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Kick off your book project, get started with GhostAI, get better at marketing, or spend the day doing all three! Free live workshops on Zoom. Saturday, June 27, 2026.

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