Run AI Locally: Models, Tools, and Guides for On-Device Inference
LocalAIZone is a hands-on resource for running AI models on hardware you own. No subscription, no prompts leaving your machine, no vendor deciding tomorrow what you get to use today. The aim is to answer one question honestly: what can this computer actually run, and what does it cost you in speed and quality to run it.
Everything here is written to stay true. You will not find benchmark tables that go stale in a month, or price comparisons that stop being right the week after they are published. What you will find is the reasoning that does not change: how memory decides what fits, what quantization trades away, and which runtime suits which job.
The four things you can run
Each pillar has a getting-started page, a models index, and a runtimes index, so you can go from nothing installed to a first result without guessing.
- Local LLMs are the deepest section. Start with getting started, then the model families worth your time and the runtimes that load them. Beyond that: quantization for making big models fit, fine-tuning for teaching one your domain, and agents for letting one use tools.
- Image generation covers getting started, the models you can run at home, and the runtimes that drive them, from node graphs to one-box web UIs.
- Video generation is the newest pillar and the most hardware-hungry of the four.
- Audio splits three ways, because the tasks barely overlap: text to speech, voice cloning and music generation. The models index covers what runs locally in each.
Start from the hardware you already have
Memory decides what you can run. Not clock speed, not core count. A model has to fit before anything else matters, and once it fits, memory bandwidth sets how fast the answers come out.
Hardware is where that gets concrete. There are pages on individual GPUs, build guides from modest to workstation, and head-to-head comparisons for the choices people actually agonise over. If you want the principle rather than the parts list, read GPU vs unified memory and will this model fit in memory.
Guides and glossary
Guides are the how-to layer that spans every pillar: choosing your first local model, picking a quantization, reading a model card, running models on a laptop, organising model files and keeping everything offline.
The glossary explains the vocabulary in one place, so no page has to stop and define it again. If a term is doing real work in a sentence, it is linked on first use: VRAM, quantization, context window, KV cache and the rest.
Where to begin
If you are starting from scratch, read choosing your first local model, then LLMs getting started. That path gets you from nothing to a model answering questions on your own machine.
If you already run models and came for the hardware question, start at hardware and the comparisons.
If you are here for images, video or audio rather than text, each pillar's getting-started page assumes nothing and links back to whatever hardware page is relevant.