All Local LLM Tools — In a Single .exe File
Node.js and Python 3.11 must be installed on your system before running the app.
Share Your FeedbackLLM Runner AIO is a comprehensive, self-contained desktop application that bundles every tool you need to run local AI models on your own hardware: Open WebUI, llama.cpp, SearXNG, Pi Coding, Vane and optional Wan2GP video generation. No complex setup, no dependency hell — just download, run, and start using AI locally. 100% open source.
Frontend interface for chatting with local LLMs
Pre-compiled CUDA 13 + Vulkan inference engine
Completely private local web search — Yandex engine included
Minimal agent harness — Web search & Advisor pre-installed (bring your own API key)
Web search integration — llama.cpp & SearXNG settings pre-configured
Optional AI image/video generation, runs locally — one-click Setup from the System tab
| VRAM | RAM | Models |
|---|---|---|
| 4 / 6 GB | 32 GB | qwen3.6-35B-A3B · gemma-4-26B · gemma-4-E4B |
| 8 GB | 32 GB | qwen3.6-35B-A3B · gemma-4-26B · gemma-4-E4B · qwen3.8-27B |
| 10 GB | 32 GB | qwen3.6-35B-A3B · gemma-4-26B · qwen3.8-27B |
| 12 GB | 32 GB | qwen3.6-35B-A3B · gemma-4-26B · qwen3.8-27B |
| 16 GB | 32 GB | qwen3.6-35B-A3B · gemma-4-26B · qwen3.8-27B (DFlash2 draft) |
| 24 GB | 32 GB | qwen3.8-27B · gemma-4-26B |
| 32 GB | 32 GB | qwen3.8-27B · gemma-4-31B |
| 4 GB | 16 GB | gemma-4-E4B · qwen3.5-4B · Ling-3.0-tiny · MiniCPM5-2B · Spark-X2.5-4B |
| 6 GB | 16 GB | gemma-4-E4B · qwen3.5-9B · Ling-3.0-tiny · MiniCPM5-2B · Spark-X2.5-4B |
Most models ship with chat, vision and coding profiles (Ling-3.0-tiny, MiniCPM5-2B and Spark-X2.5-4B ship with a single profile). All presets can be added, edited, or removed directly from the GUI — no manual INI editing needed.
A single 2.28 GB .exe file — double-click and wait. Everything is set up automatically within a local virtual environment (venv).
Detects your GPU/VRAM and applies the matching hardware profile (VRAM options: 4, 6, 8, 12, 16, 24, 32 GB).
Select your auto-detection profile and click Model Download — only models that fit your VRAM are downloaded and configured.
Add, edit, or remove models straight from the System tab — including draft/speculative URLs. Preset INIs are rewritten safely and download URLs stay in sync.
Wan2GP: install, start, stop and monitor the AI image/video server from the same screen (default port 7860).
Parameters fine-tuned for Qwen and Gemma models to maximize token speed and eliminate formatting or context loop issues.
qwen3.8-27B ships with a DFlash2 draft model for higher token speed in supported presets.
You can review the entire source code on GitHub.

http://localhost:1234http://localhost:3000http://localhost:8080http://localhost:3001http://localhost:7860Download LLM-Runner-AIO.exe (2.28 GB) from Hugging Face
.exe: double-click to execute. .RAR: extract, then run run.bat first — it installs dependencies, configures Pi Coding and creates a desktop shortcut
Click System Detection, then Model Download — models are auto-configured for your VRAM
Launch all services and access at http://localhost:3000
| Requirement | Minimum | Recommended |
|---|---|---|
| OS | Windows 10/11 | Windows 11 |
| RAM | 8 GB | 16 GB+ |
| VRAM | N/A | 4 GB+ |
| Python | 3.11 (required) | 3.11 (required) |
| Node.js | Latest (required) | Latest LTS (recommended) |
| Wan2GP (optional) | N/A | Few extra GB disk + CUDA/driver matching your GPU generation |
Note: Python 3.11 and Node.js must be installed on your system before running the app. The optional Wan2GP video service needs a few extra GB of disk space and the CUDA/driver version matching your GPU generation (exact requirements are shown in the setup confirmation window).
This project would not be possible without the incredible work of:
Download LLM Runner AIO and start running local LLMs instantly.
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