Hardware-aware · Open source · Updated weekly
The hub for local LLMs, install guides and a hardware picker.
You can run AI models like the ones behind ChatGPT or Claude directly on your laptop or desktop, without sending your data anywhere. RunLocal shows you which open source model to choose for your hardware, which free software to install (Ollama, LM Studio, llama.cpp), and how to get started in about ten minutes. No prior knowledge required.
New to this? Start with the glossary for plain-language definitions, or jump straight to the hardware picker to see what your computer can run.
A short list, opinionated. Full catalog in the directory.
Meta AI · United States
Mixture-of-experts architecture. Scout pushes context to 10M tokens, Maverick targets frontier-tier reasoning. Custom license restricts use above 700M MAU and forbids training competitors.
Alibaba (Qwen team) · China
The most permissive frontier-class family for smaller sizes. Strong on Asian languages, competitive on English code and reasoning benchmarks.
DeepSeek AI · China
Topped open-source leaderboards on SWE-Bench and GPQA Diamond in early 2026. The MIT-licensed core variant is the most permissive of the top-tier open weights.
Mistral AI · France (EU)
Released April 2026. The most credible non-Chinese, non-American option at frontier level. Mistral remains the leading European LLM lab by capability.
Snapshot · May 15, 2026
Auto-curated from the Hugging Face Hub by a weighted mix of downloads, community likes and recency. Refreshes weekly via GitHub Action. License tier is a quick visual hint, not legal advice.
deepseek-ai
MIT
moonshotai
Custom license
deepseek-ai
MIT
zai-org
MIT
MiniMaxAI
Custom license
zai-org
MIT
deepseek-ai
MIT
MiniMaxAI
Custom license
Runtimes, GUIs and inference servers, with their real trade-offs.
Runtime
The fastest way to get a local LLM running with one command.
Runtime
Maximum control and the broadest hardware coverage in the open ecosystem.
GUI
Browsing, comparing and chatting with local models in a desktop GUI.
Server
Production-grade inference with concurrent users and high throughput targets.
From zero to a working local LLM in about ten minutes, with the commands that actually matter and the gotchas nobody warns you about.
How to compile llama.cpp with the right backend for your hardware, pick a GGUF quantization that fits your RAM, and serve an OpenAI-compatible endpoint.
A practical walkthrough for using LM Studio to download, compare and serve local models, plus when LM Studio is the wrong tool.
Q4, Q5, Q8 and the rest of the GGUF zoo, with a practical decision rule that holds up across hardware classes.
Unified memory, raw VRAM, and the workloads where each wins. A practical comparison that goes beyond benchmark snippets.
Rolling releases, immutable variants, and an honest line between community Linux and paid enterprise. A practical look at when openSUSE earns its place in an AI stack.
Five frontier-class releases in the last thirty days, three of them from Chinese labs. A short tour of where the field actually is.
Ollama, llama.cpp, LM Studio and vLLM solve different problems. A practical map of when to reach for which, and why it matters more than benchmark numbers.
The big cloud services are easier to start with. But there are real reasons to do it yourself. Three of them.
What you type and what the model answers never leave your computer. Handy when you are working with personal notes, client documents, internal code, or anything you would not paste into a public website.
The model file lives on your disk. If the company that made it shuts down, raises prices, or simply changes its terms, your setup keeps working. The model you download today still runs in 2030 if your computer does.
Cloud AI charges per use. Local AI costs you the price of your computer, plus electricity. After the first month, the marginal cost of an extra question is essentially zero.
