#how-to

The deep-dive on use-case #5. Classify, extract, summarize, or transform a pile of documents headlessly — running the model against a queue, not a chat. Where local AI's cost-at-scale argument actually bites, the throughput math, and the honest 'one stream at a time' limit.

The deep-dive on use-case #3. Wire a local speech-to-text model, a local LLM, and a local TTS engine into a voice assistant that works offline, has no wake-word corporation listening, and runs on hardware you own. Architecture, latency budget, and the honest '1–2 seconds' reality.

The deep-dive on use-case #2. Drop contracts, research dumps, or internal docs into a local RAG stack and the model reads, retrieves, and answers — with nothing leaving your disk. Architecture, model choices, the honest limits of local retrieval, and a working starting config.

The deep-dive on use-case #1 from '5 things you can do with a local LLM.' A working coding agent — refactors, tests, explains codebases — running on a local model, with your source never leaving your machine. Setup, model choice, where it's good, and where it falls down.

A working private chat assistant — model, web UI, and OpenAI-compatible API — running entirely on hardware you own, for the price of one card. The shortest path from zero to 'it works,' with the honest limits and where to go next.

How to make ComfyUI actually fast on a card you can afford. Dynamic VRAM is the big 2026 leap, but the quantization and attention-backend choices matter as much. Here's what each optimization does, when it helps, and the honest trade-offs.

Running Wan, Hunyuan Video, and CogVideoX locally — what's actually runnable on consumer GPUs, what isn't, and the optimizations that help. The honest version, because video is where local hardware hurts most.

What it actually takes to run Flux and Stable Diffusion on your own GPU — VRAM floors by model, GPU picks by budget tier, and the five optimizations that make marginal cards viable. No 'best GPU' list — the math.

Yes, really. A Q4_K_M 30B coder model fits in 12GB of VRAM and runs at usable speed on a single Intel Arc B580. Here's the recipe — with the numbers to back it.