Install a friendly runner like Ollama or LM Studio, pick a small-to-mid model that fits your hardware, and run one command (or click download) — you'll be chatting with a fully local model in minutes, for free.
Running your first open model locally is far easier than most people expect — no machine-learning knowledge required, and it's free. The whole thing comes down to installing one friendly tool, picking a model that fits your hardware, and starting it. Here's the honest, no-jargon path from nothing to chatting with a local model.
Step one: pick a runner. The two easiest are Ollama and LM Studio. Ollama is command-line-first: you install it, then type a single command to download and run a model. LM Studio is a graphical desktop app: you install it, search for a model in its built-in browser, click download, and chat in a window. Both run on macOS, Windows, and Linux, both handle all the technical details for you (downloading the right file, picking a sensible quant, using your GPU if you have one), and both are free. Choose Ollama if you're comfortable with a terminal, LM Studio if you prefer a visual app.
Step two: pick a model that fits your hardware. This is the one choice that matters for a good first experience. Use the simple rule — about 0.5 GB per billion parameters at the default 4-bit quant, plus a few gigabytes of headroom. If you have an 8 GB card or a 16 GB Mac, start with a 7-8B model. If you have more VRAM, you can go bigger. For a first run, a well-known 7-8B instruct model is an ideal starting point: capable, fast, and comfortable on modest hardware. Make sure you grab an "instruct" or "chat" version (not a raw "base" model), because the instruct version is the one tuned to follow your instructions and hold a conversation — a base model will seem "broken" because it only completes text rather than answering you.
Step three: run it. In Ollama, that's one command like "ollama run" followed by the model name — it downloads the model (a few gigabytes, so give it a minute) and drops you straight into a chat prompt. In LM Studio, you click the downloaded model and open a chat window. Either way, you're now talking to a model running entirely on your own machine. The friendly tools apply the correct chat template automatically, so the model behaves like an assistant out of the box.
That's genuinely it — the barrier to entry is a small download and a single command or click. A few things to know as you get comfortable: the model runs offline once downloaded, so you can disconnect the internet and it keeps working; your prompts and its replies never leave your machine, so it's private; and it costs nothing beyond the electricity to run your computer. If the first model feels slow, try a smaller one or a lower quant; if it feels not-smart-enough and you have VRAM to spare, try a larger one or a higher quant. Experimenting is free.
A couple of next steps once the basics click. Both runners can start a local server that speaks an OpenAI-compatible API, which lets you point other apps, scripts, or coding tools at your local model instead of a paid cloud service — a powerful, free, private backend for whatever you build. And you can download several models to compare, though each takes real disk space, so prune the ones you don't keep. As you learn, the concepts worth understanding next are quantization (which version to download), VRAM (what fits), and the difference between base and instruct models — all covered in the glossary.
The honest promise here is that local AI is a genuine, free, private option that anyone can start today on hardware they already own. The only real limit is your VRAM, which caps how big a model you can run comfortably.
Spanvero helps you pick a first model that will run well on your specific machine. Use /calculator/ to check what fits your hardware and see its $0-local cost; browse beginner-friendly models by your VRAM at /models/8gb-vram/ or /models/16gb-vram/, or the smallest at /best/best-small-llms/. For the tools, see /learn/ollama/ and /learn/lm-studio/; for what to download, /learn/which-quantization-should-i-use/ and /learn/base-vs-instruct/.
Ollama · LM Studio · What quantization should I use? · Base vs instruct model · Do I need a GPU to run local AI? · How do I run AI privately and offline? · What LLMs can I run on 8GB of VRAM? · How do I choose which AI model to run?
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