Local AI: run open source models on your own computer.

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.

The author of Redis wrote an inference engine in C that runs essentially a single model, and it does things the general-purpose runtimes cannot. What ds4 gets right, and what it costs in hardware.

Alibaba published two models in the same generation this month. One has four million downloads, the other twenty-seven thousand. The difference is not capability.

A 35B MIT-licensed model from an account we'd never heard of just outran everything else on Hugging Face's trending list except the evergreen giants. Here is what we checked, what we couldn't verify, and why it isn't in the directory.

A quiet mid-cycle refresh went straight to the top of our weekly trending list. What a dated checkpoint tag means if you already run the model it's replacing.

A 2.8-trillion-parameter model with public weights that almost nobody can run. What the largest open weight ever released actually means for people who run AI locally.

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.