Kolosal AI

https://github.com/user-attachments/assets/589cfb48-f806-493d-842b-3b6953b64e79

Kolosal AI is an open-source desktop application designed to simplify the training and inference of large language models on your own device. It supports any CPU with AVX2 instructions and also works with AMD and NVIDIA GPUs. Built to be lightweight (only ~20 MB compiled), Kolosal AI runs smoothly on most edge devices, enabling on-premise or on-edge AI solutions without heavy cloud dependencies.

• License: Apache 2.0
• Developer: Genta Technology
• Community: Join our Discord

Key Features

1. Universal Hardware Support
2. AVX2-enabled CPUs

3. AMD and NVIDIA GPUs

4. Lightweight & Portable

5. Compiled size ~20 MB

6. Ideal for edge devices like Raspberry Pi or low-power machines

7. Wide Model Compatibility

8. Supports popular models like Mistral, LLaMA, Qwen, and many more

9. Powered by the Genta Personal Engine built on top of Llama.cpp

10. Easy Dataset Generation & Training

11. Build custom datasets with minimal overhead
12. Train models using UnsLOTH or other frameworks

13. Deploy locally or as a server in just a few steps

14. On-Premise & On-Edge Focus

15. Keeps data private on your own infrastructure
16. Lowers costs by avoiding expensive cloud-based solutions

Use Cases

• Local AI Inference: Quickly run LLMs on your personal laptop or desktop for offline or on-premise scenarios.
• Edge Deployment: Bring large language models to devices with limited resources, ensuring minimal latency and improved privacy.
• Custom Model Training: Simplify the process of data preparation and model training without relying on cloud hardware.

License Notice

Although the Kolosal AI is entirely Open-Source, and can be distributed for free or commercial purposes under Apache 2.0 License, the main engine (the Genta Inference Engine Personal) can not be distributed for commercial purposes. For those who are planning to use Kolosal AI for commercial purpose, must contact our team at rifky@genta.tech, or you can implement your own or other inference engine on Kolosal AI, or by being a contributor to Kolosal AI. This is intended to motivate contribution by companies to the Kolosal AI community.

Genta Inference Engine Personal © 2025 by Rifky Bujana Bisri is licensed under CC BY-NC-SA 4.0. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/

Credits & Attribution

Kolosal AI uses or references the following third-party projects, each licensed under their respective terms:

• Dear ImGui (MIT License)
• llama.cpp (MIT License)
• nativefiledialog-extended (zlib License)
• nlohmann/json (MIT License)
• stb libraries (Public Domain or MIT License)

These projects are distributed under their own licenses, separate from Kolosal AI. We are not affiliated with nor endorsed by the above entities.

About Genta Technology

We are a small team of students passionate about addressing key concerns in AI such as energy, privacy, on-premise, and on-edge computing. Our flagship product is the Genta Inference Engine, which allows enterprises to deploy open-source models on their own servers, with 3-4x higher throughput. This can reduce operational costs by up to 80%, as a single server optimized by our engine can handle the workload of four standard servers.

Get Involved

1. Clone the Repository: [https://github.com/Genta-Technology/Kolosal]
2. Join the Community: Ask questions, propose features, and discuss development on our Discord.
3. Contribute: We welcome pull requests, bug reports, feature requests, and any kind of feedback to improve Kolosal AI.

How to Compile

1. Project Overview
2. Directory Structure
3. Prerequisites
4. Cloning and Preparing the Repository
5. Configuring the Project with CMake
6. Building the Application
7. Running the Application
8. Troubleshooting

Project Overview

• Name: Kolosal AI (Desktop application target is KolosalDesktop)
• Language Standard: C++17
• Build System: CMake (version 3.14 or higher)
• Dependencies (automatically handled by the provided CMakeLists.txt, if placed in correct directories):
• OpenGL
• OpenSSL
• CURL
• GLAD
• Native File Dialog Extended
• genta-personal engine libraries (InferenceEngineLib, InferenceEngineLibVulkan)
• ImGui (provided in external/imgui)
• Other external libraries: stb, nlohmann/json, icons, etc.

Directory Structure

A simplified look at the important folders/files:

KolosalAI/
├─ cmake/
│   └─ ucm.cmake                 # Utility script for static runtime linking
├─ external/
│   ├─ curl/                     # Pre-built or source for cURL
│   ├─ glad/                     # GLAD loader
│   ├─ genta-personal/           # genta-personal engine includes/libs
│   ├─ imgui/                    # ImGui source
│   ├─ nativefiledialog-extended # Native File Dialog Extended
│   ├─ nlohmann/                 # JSON library
│   ├─ stb/                      # stb (single-file) headers
│   └─ fonts/                    # TrueType fonts
├─ assets/
│   ├─ logo.png
│   └─ resource.rc               # Windows resource file
├─ source/
│   └─ main.cpp                  # Entry point for KolosalDesktop
├─ include/
│   └─ ... (additional headers)
├─ models/
│   └─ ... (model.json configuration files used by the inference engine to download, save, and load the model engine)
├─ CMakeLists.txt
├─ README.md                     # You are here!
└─ ...

Prerequisites

1. CMake 3.14 or above
   Download from https://cmake.org/download/.
2. A C++17-compatible compiler
3. For Windows, Visual Studio 2019/2022 (MSVC) or MinGW-w64 with GCC 7+.
4. For other platforms, an equivalent compiler supporting C++17.
5. Git (optional, but recommended for cloning and submodule management).
6. OpenSSL, CURL
7. On Windows, you can place the pre-built bins/headers inside external/openssl and external/curl (or anywhere you prefer, just ensure CMakeLists.txt sees them).
8. (Optional) Vulkan SDK if you plan to use the Vulkan-based inference engine.

Cloning and Preparing the Repository

1. Clone the repository:

bash git clone https://github.com/Genta-Technology/Kolosal.git cd KolosalAI

1. (Optional) Update submodules:
   If any external libraries are handled as Git submodules, initialize them:

bash git submodule update --init --recursive

1. Check external dependencies:
   Ensure the external folder contains:
2. curl with include/, lib/, and bin/ (Windows).
3. openssl or that OpenSSL is installed system-wide.

4. The genta-personal engine in place if not fetched from elsewhere.

5. Folder structure verification:
   Verify that folders like nativefiledialog-extended, imgui, etc., are present inside external/.

Configuring the Project with CMake

You can perform either an in-source or out-of-source build, but out-of-source is recommended. Below is an example of an out-of-source build:

1. Create a build folder:

bash mkdir build cd build

1. Run CMake:
   By default, this will generate build files for your platform’s default generator (e.g., Visual Studio solution files on Windows, Makefiles on Linux, etc.):

bash cmake -S .. -B . -DCMAKE_BUILD_TYPE=Release

or explicitly (for Visual Studio multi-config):

bash cmake -S .. -B . -G "Visual Studio 17 2022" -A x64

• -DDEBUG=ON can be used if you want to build a debug version:

  bash cmake -S .. -B . -DCMAKE_BUILD_TYPE=Debug -DDEBUG=ON

• Check for any errors during configuration, such as missing libraries or headers. Resolve them by installing or copying the required dependencies into the correct location.

Building the Application

After successful configuration:

• On Windows with Visual Studio:
  Open the generated .sln file inside build/ and build the solution. Or build from the command line using:

bash cmake --build . --config Release

• On other platforms (e.g., using Make or Ninja):

bash cmake --build . --config Release

Note:
The POST_BUILD commands in CMakeLists.txt will copy the necessary DLLs, fonts, assets, and models into the final output folder (e.g., build/Release/ or build/Debug/, depending on your generator).

Running the Application

1. Locate the output:
   Once the build completes, you should find the executable (e.g., KolosalDesktop.exe on Windows) in a directory such as:
2. build/Release/ (Visual Studio).

3. build/ (single-config generators like Make).

4. Check for required files:
   The post-build commands should have copied:

5. Fonts (/fonts folder next to the exe).
6. Assets (/assets folder next to the exe).
7. Models (/models folder next to the exe).
8. OpenSSL and InferenceEngine DLLs (Windows).
9. cURL DLL(s) (Windows).

Make sure these folders and files are present in the same directory as KolosalDesktop.exe.

1. Double-click or run from terminal:

bash cd build/Release ./KolosalDesktop.exe

1. Enjoy Kolosal AI!

Troubleshooting

1. OpenSSL or CURL not found
2. Make sure you have them installed or placed in external/openssl and external/curl respectively.
3. Check environment variables like OPENSSL_ROOT_DIR or CURL_ROOT_DIR if needed.

4. Update CMAKE_PREFIX_PATH if you’re placing these libraries somewhere non-standard.

5. InferenceEngine libraries not found

6. Verify the path external/genta-personal/lib actually contains InferenceEngineLib.lib or InferenceEngineLibVulkan.lib (on Windows).

7. Adjust find_library paths in CMakeLists.txt if your structure differs.

8. Missing Vulkan SDK

9. If you plan to use the Vulkan-based inference engine, ensure Vulkan SDK is installed and available in your PATH or that CMake can find it.

10. ImGui not found

11. Ensure external/imgui folder is not empty.

12. If you see compilation errors referencing ImGui headers, check that target_include_directories in CMakeLists.txt still points to the correct path.

13. Resource or Icon issues on non-Windows

14. The assets/resource.rc file is Windows-specific. For Linux/macOS builds, you can comment out or remove references to .rc if they cause issues.

15. Runtime errors due to missing DLLs or dynamic libraries

16. Confirm that the post-build step copies all required .dll files next to the executable.
17. On Linux/macOS, ensure .so/.dylib are in the library search path or same folder.