LocaLM
Run large language models on your own machine. Offline, private, and yours.
LocaLM downloads and runs LLMs locally: GGUF models through llama.cpp and HuggingFace models through transformers, on AMD, NVIDIA, Intel, Apple Silicon or CPU. It ships a chat GUI, an OpenAI-compatible API, a coding agent, RAG over your own documents, and an MCP server, with everything off by default and nothing leaving your machine.
Install
pip install localm
localm setup-llama
setup-llama detects your GPU and provisions the matching llama.cpp runtime.
It needs no vendor toolkit: NVIDIA gets a self-contained CUDA build, AMD on
Windows a bundled ROCm build, Intel and toolkit-less AMD a Vulkan build, Apple
Silicon Metal, and anything else CPU.
Then pull a model and talk to it:
localm pull unsloth/Qwen3-4B-Instruct-2507-GGUF:Q4_K_M
localm run Qwen3-4B-Instruct-2507
Or open the graphical interface:
localm gui
Python 3.12 is required. localm doctor reports what is installed and what is
missing at any point.
Where data lives
Set LOCALM_HOME to choose where models, chats and settings are stored. Left
unset, it defaults to a directory inside the Python environment you installed
into (not a per-user directory); localm info prints the path actually in
use.
The self-contained installer
The pip package installs LocaLM into an environment you already manage. The installer on GitHub instead provisions its own Python and its own private environment, adds a desktop launcher and a native app window, and walks you through choosing plugins. If you would rather have that, or you are not on Python 3.12, use it:
https://github.com/Matlan1/localm
Documentation, issues and source
https://github.com/Matlan1/localm
AGPL-3.0-or-later.
1 maintainer
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