Build Your Own Private AI Lab Track

Build Your Own Private AI Lab

Pro Track · ~135 min total

Run frontier open-weight models on your own hardware: chat, coding, images, video, and voice with zero per-token fees and full data privacy. Covers the hardware buying decision (NVIDIA, Apple Silicon, and AMD, with NVIDIA as the default), llama.cpp on CUDA, Metal, or Vulkan, picking and quantizing GGUF models, a ChatGPT-style UI with Open WebUI, local image/video with ComfyUI, voice cloning, wiring local models into agents and RAG, and the honest call on when local beats cloud. Built and benchmarked on real 2026 hardware, including the AMD ROCm gotchas nobody else documents.

What You'll Learn

8 modules covering everything you need.

Why Run AI Locally (and What Hardware You Actually Need)

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The real case for local AI: privacy, zero per-token cost, no rate limits, full control. The three hardware paths (NVIDIA as the default, Apple Silicon, AMD), the memory-and-bandwidth math that decides everything, and named build recommendations for June 2026.

18 min read5 sections

Your Inference Engine: llama.cpp

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Why llama.cpp is the foundation, choosing your backend (CUDA for NVIDIA, Metal for Apple, Vulkan for AMD), installing on Windows, running your first model, and exposing an OpenAI-compatible API with llama-server.

16 min read5 sections