#how-to

ComfyUI is the most powerful local image-generation tool — and the one with the steepest learning curve. Here's the honest on-ramp: what the node graph actually is, the minimum workflow to get an image, where beginners get stuck, and when to use ComfyUI versus a simpler alternative.

DeepSeek's reasoning models are among the most capable open weights you can run — and the V4 generation is enormous. Here's what actually fits on consumer hardware, what doesn't, and the honest setup path for the DeepSeek models that matter for local inference.

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.

A start-to-finish guide to running LLMs on your own hardware in 2026 — pick the right GPU, choose a backend, pick a quantization, and pick a runtime. Every recommendation is backed by measured benchmarks, not guesses. For beginners and builders.

Every coding-LLM roundup ranks models by leaderboard score. Almost none tell you what the score costs in tok/s when you step down a quant tier — which is the only trade-off that matters once the model has to fit on a card you own. Here's the measured table for the coder we run, across three quant tiers, on the same hardware.

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.

Every backend explainer describes CUDA, ROCm, Vulkan, and SYCL correctly and then tells you to 'pick what works on your card.' Almost none measure them on the same hardware. We did, across nine benchmark records. The backend gaps are large, vendor lock-in is real, and one documented failure is worth a thousand abstract paragraphs.

Every 'home AI lab' guide describes a single workstation. None describe running models across the machines you already own. Here's the four-node cross-vendor fleet we run — Intel Arc, dual AMD, AMD APU, dual NVIDIA — the routing pattern that ties it together, and what a fleet gets you that a single big box doesn't.

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.

The 'Ollama is easier, llama.cpp is faster' framing is from 2024 and it was correct then. What it never came with was the actual measured gap on the same card with the same weights. Here's that gap — across four backend configurations on a single R9700, plus the one result that looks like an Ollama win and isn't.

Every 'how much power does local AI use' piece multiplies GPU TDP specs by hours and calls it a day. Almost nobody measures actual wall-power during inference. We measured a narrow slice — and the headline finding is that quantization barely moves power draw. The honest version of what we have, what we don't, and the rough cost math.

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.

Every speculative decoding explainer calls it a 'free speedup.' Almost none measure the actual multiplier on consumer hardware or show the accept-rate curve across context lengths. Here's both — Qwen3.6-27B on the same R9700 with and without MTP, plus the long-context sweep that explains when the speedup holds and when it shrinks.

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.