FreeToken allows you to run local models that exceed your GPU’s VRAM by combining graphics memory, system RAM, and CPU processing.
It’s an open-source inference engine licensed under Apache-2.0, with a desktop application and a command-line interface. For developers, one of its most useful features is the local server: it offers endpoints compatible with OpenAI and Anthropic APIs that you can connect existing clients to. Official Repository
The practical question is what model your entire machine can support and how it will respond during real-world use.
How Does FreeToken Run a Model That Exceeds VRAM?
FreeToken keeps a set of model experts in system RAM and uses GPU memory as a cache, distributing work between CPU execution and GPU transfers.
This is especially useful with Mixture-of-Experts models, or MoE. These activate only a portion of their parameters for each token, but the complete set of experts still needs storage. Having fewer active parameters reduces computation; the remaining weights still take up space.
FreeToken also addresses repeated prompt processing during agent sessions, where tool calls and changes to history can force recalculation of part of the context. Its design combines expert caching with reuse of already-processed context. Technical Paper
What Hardware Does FreeToken Need?
To install the CLI using the documented procedure, you need Linux x86_64, an NVIDIA GPU, and a compatible CUDA environment.
As of September 11, 2026, the installation guide specifies:
- NVIDIA driver r580 or later.
- CUDA 13, including the toolkit with
nvccavailable in yourPATHto compile kernels on first use. - Python 3.10 or later.
There is no single amount of RAM that works for all models. The checkpoint chosen, its precision, and the workload determine the memory required. Review system RAM and available storage alongside VRAM before selecting a model. Installation Guide
The technical paper shows why that distinction matters: the test server with an RTX 5090 combines 32 GB of VRAM with 180 GiB of system RAM. Several GPU comparisons use rented servers with CPU execution limited to approximate the bandwidth of a consumer machine. A test using a consumer GPU does not mean the complete machine is equivalent to your PC. Evaluation Environment
Does FreeToken Work on Windows or Mac?
The README offers a desktop application for Windows and Linux; the CLI installation guide documents Linux.
These are separate installation paths. As of September 11, 2026, the cited documentation does not provide an installation procedure for macOS. Follow the instructions for the path you will use: Linux commands cannot be automatically transferred to any platform. Desktop Application Information
If you’re using Apple Silicon, you can also check our Swiftlet coverage.
How Do I Install FreeToken on Linux?
Once you meet the hardware and CUDA requirements, create a Python environment and install the package with its acceleration dependencies.
The project recommends using uv:
uv venv && source .venv/bin/activate
uv pip install "freetoken[accel]"
Verify that the CLI is available:
ft --version
These commands come from the official installation guide. Installing the package does not by itself download a suitable model or confirm that its weights fit on your machine.
How Do I Use FreeToken as a Local Server?
Start the server with a compatible checkpoint and check what model identifier it exposes.
The following is the example documented by the project. As of September 11, 2026, the list of compatible models includes this Qwen checkpoint; the path assumes you’ve already saved it in that directory:
ft serve --model ~/models/Qwen3.6-35B-A3B
When the server is ready, check its model list:
curl http://127.0.0.1:1919/v1/models
Use the identifier returned in your client. The getting started guide documents endpoints compatible with OpenAI and Anthropic. Check the current list of compatible models before downloading weights. As of September 11, 2026, multimodal checkpoints are served as text only. Getting Started Guide
How Do I Evaluate FreeToken’s Performance on Your Machine?
Measure the time to first token and responsiveness over several turns with a workload similar to yours.
The authors report performance improvements, but these are benchmarks from the project itself, without independent validation in this note. Their evaluation distinguishes generation speed from time to first token and does not compare total time to complete tasks between engines, because agents follow different paths. Generating tokens faster is not enough to show that an agent finishes your work sooner. Evaluation Methodology
Start with a compatible checkpoint whose requirements you can meet with headroom. Test a short prompt, longer input, and a sequence of follow-up questions. Record the model, precision, RAM, GPU, and observed wait times.
The practical value of FreeToken is in giving you another way to leverage the combined resources of your machine. The decision to incorporate it into your workflow should depend on how it solves your real tasks.