Practical overview
When it fits
Use FreeToken when the goal is to serve an open-weight MoE model on personal hardware and make use of available GPU, CPU and host-memory resources.
Who it is for
- People who want to run open-weight MoE models on a personal computer.
- Developers prototyping local inference for desktop applications or edge workflows.
- Builders connecting a local model service to coding or tool-calling agents.
Documented uses
- Run supported open-weight MoE models locally.
- Chat with a locally served model through the desktop interface.
- Tune the engine through the desktop app.
- Connect local serving to compatible coding and tool-calling clients.
Requirements & limits
The README documents a desktop app for Windows and Linux, plus CLI installation with uv or pip and a source-build path. The repository identifies the project as Apache-2.0 licensed.
- The project names NVIDIA RTX 30, RTX 40 and RTX 50 series GPUs among supported hardware.
- Model and quantization support should be checked against the project's supported-model documentation; the README lists MXFP4, NVFP4, FP8 and BF16 formats.
- The desktop app provides setup, model running, chat and engine tuning; the CLI and source-build routes are documented separately.
- The project describes Anthropic- and OpenAI-compatible APIs for agent integrations.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install FreeToken from its repository with uv
Prepare a source installation of FreeToken using its documented uv workflow.
Before you start
- Git for cloning the repository.
- uv, the installation tool recommended by the project.
- A shell that supports the documented source .venv/bin/activate command.
Steps
Clone the project and enter its directory: git clone https://github.com/FlashML-org/FreeToken.git && cd FreeToken
Create and activate the virtual environment: uv venv && source .venv/bin/activate
Install the source package with its acceleration extra: uv pip install -e ".[accel]"
Expected outcome
If the commands succeed, the repository package is installed in the activated virtual environment.
Guide limitations
- This procedure covers installation, rather than model configuration or inference.
- Consult the project's Quick start link for the next stage. These instructions have not been executed or benchmarked by OpenViST.
Compare options for Model inference and serving
These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.
| Project | Documented purpose | Platform | Access and requirements |
|---|---|---|---|
| Freetoken | Run supported open-weight MoE models locally. | Self-hosted | The README documents a desktop app for Windows and Linux, plus CLI installation with uv or pip and a source-build path. The repository identifies the project as Apache-2.0 licensed. |
| Inferoxy | Serve requests to Dockerized computer-vision models through supported bridges. | Docker, Linux, Self-hosted, Server | |
| FriendliAI | Send inference requests to a hosted open-weight model through a Model API. | Web, Cloud | The official documentation lists Model APIs and Dedicated Endpoints. It also describes Model APIs as serverless and pay-per-token; the evidence does not specify prices. |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 21 October 2026
- Original website title
- FlashML-org/FreeToken · Official homepage title · retained in its source language