Anything LLM
Run open LLMs on your own hardware — practical, private, local AI.
Read the blog →You don’t need an API key, a monthly bill, or someone else’s data center to run a capable language model. Open-weight models now fit on the hardware you already own — a gaming GPU, an Apple Silicon laptop, sometimes just a healthy amount of RAM. Anything LLM is about the practical side of that: getting models running locally, keeping your data on your own machine, and knowing which trade-offs are worth making.
This is a site for tinkerers and developers, not a concept course. We assume you’d rather run the command than read the theory, so the guides here are hands-on: what to install, what hardware actually matters, and where local inference genuinely beats sending everything to the cloud.
What you’ll find here
- Getting models running — practical setup with tools like Ollama, LM Studio, and llama.cpp, from first
runto something you’d actually use. - Hardware and memory — how much VRAM and RAM a model really needs, why quantization changes the math, and how to right-size a model to your machine.
- Quantization and formats — understanding GGUF, quantization levels, and the quality-versus-size trade-off in plain terms.
- Local vs cloud — an honest look at privacy, cost, latency, and control, and when self-hosting is the wrong call.
Everything here is tool-agnostic and vendor-neutral. This tooling moves fast — versions, model releases, and hardware support change month to month — so we explain the durable ideas and always point you to the official docs for current specifics.
Ready to run your first model?
Latest posts
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Run Your First Local LLM with Ollama
A hands-on getting-started guide to running an open-weight LLM locally with Ollama — install, pull a model, chat, and call the local API.
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How Much VRAM and RAM Do You Actually Need to Run an LLM?
A practical guide to sizing local LLMs: how to estimate VRAM and RAM from parameters and quantization, plus what happens when a model doesn't fit.