LocalLLM is a lightweight Tauri desktop control panel for running
llama.cpp locally. It helps you discover GGUF models, configure
llama-server, preview the launch command, save per-model presets, and open
the local Web UI.
- Discover
llama-server,llama-cli,hf, andhuggingface-clifromPATH. - Install supported prebuilt
llama.cppWindows and Ubuntu Linux release assets from inside the app. - Save executable paths, model directory, manual GGUF entries, and server defaults.
- Scan a model directory recursively for
.gguffiles. - Add manually selected
.gguffiles outside the model directory. - Download GGUF files from Hugging Face through
hf download. - Preview the exact
llama-servercommand before launch. - Explain the active command in a compact helper below the preview.
- Estimate VRAM from GGUF metadata when available.
- Configure runtime, KV cache, GPU/memory, sampling, speculative decoding, chat/reasoning, multimodal, Jinja, tools, embeddings, and verbosity options.
- Save per-model presets and automatically return to the selected model's own preset when changing models.
- Start, stop, and open the local llama.cpp server.
- Run
llama-serverhidden in the background with log capture, or in a visible terminal. - Warn before starting when another background
llama-serverprocess is already running. - Reset LocalLLM's saved settings/cache without deleting downloaded models.
The built app does not require Node.js or Python to run.
You only need the tools you want LocalLLM to control:
llama-serveris required to serve models.llama-cliis optional.hforhuggingface-cliis optional for Hugging Face downloads. LocalLLM can usecurlfor a single selected file, but glob patterns such as*.ggufneed the Hugging Face CLI.- GGUF model files.
On Windows and Ubuntu-based Linux distributions, the app installs prebuilt
official llama.cpp release assets from the Settings panel. Ubuntu Linux
installs use the official *-bin-ubuntu-*.tar.gz packages and require curl
and tar on PATH; LocalLLM does not compile llama.cpp from source. You can
also browse to your own llama-server and llama-cli binaries.
- Open LocalLLM.
- In Settings, set or install
llama.cpp. - Set the model directory where your
.gguffiles live. - Press Rescan to load models.
- Pick a model from the model dropdown or model list.
- Adjust runtime options and presets.
- Press Start.
- Press WebUI or switch to the Web UI tab after the server starts.
Useful controls:
- New Preset resets the current model preset to a clean default configuration.
- Profile editor lets you create, save, select, and delete presets.
- Tools all, Jinja, Embeddings, and Verbose are quick toggles near Extra args.
- Extra args is appended to the generated
llama-servercommand. - Reset everything in Settings clears LocalLLM settings, presets, cache, logs, theme, and layout. It does not delete GGUF files or your llama.cpp install.
Install:
- Node.js 20 or newer
- Rust stable
- Tauri prerequisites for your OS
Install JavaScript dependencies:
npm installRun in development mode:
npm run tauri devRun the frontend-only build:
npm run buildRun the current UI regression test:
npm run test:model-selectionBuild for the current operating system:
npm run tauri buildWindows bundles are written to:
src-tauri/target/release/localllm.exesrc-tauri/target/release/bundle/nsis/src-tauri/target/release/bundle/msi/
Linux and macOS builds should be produced on their native operating systems or with CI runners for those systems. Tauri desktop bundles are OS-specific.
Install Node.js, Rust, and Tauri's Linux system dependencies. On Ubuntu/Debian, the dependency list depends on your distro version, but commonly includes WebKitGTK, GTK, AppIndicator, librsvg, curl, wget, and build tools.
Then run:
npm install
npm run tauri buildLinux artifacts are produced under src-tauri/target/release/bundle/.
Build on macOS with Xcode Command Line Tools installed:
npm install
npm run tauri buildmacOS artifacts are produced under src-tauri/target/release/bundle/.
Generated folders such as node_modules, dist, and src-tauri/target are
ignored and should not be committed.
