Install

Kinetic runs on Windows 10 and 11 and on Linux, on x86-64. Pick one way to install it.

Install script

irm https://raw.githubusercontent.com/Veloresearch/kinetic/main/scripts/install.ps1 | iex

curl -fsSL https://raw.githubusercontent.com/Veloresearch/kinetic/main/scripts/install.sh | sh

The scripts download the latest release (the full build, which runs on NVIDIA GPUs through CUDA, on AMD and Intel GPUs through Vulkan and on the CPU), check it against the release's SHA256SUMS and install it for the current user: into %LOCALAPPDATA%\Programs\kinetic, put on the user PATH, on Windows, and into ~/.local/bin on Linux. -Version 0.1.0 / --version 0.1.0 picks a release and -Cpu / --cpu the smaller CPU-only build.

Release binaries

From the releases page, take the archive for your system:

The cuda archives also run on machines without a GPU, on the CPU; they are the right choice unless you want the smallest download. They need only the NVIDIA driver, not the CUDA toolkit.

Each archive holds the kinetic binary, the C library and header (lib/, include/), the license files and a SHA256SUMS of its contents. The release also has a SHA256SUMS for the archives themselves. Check the download before you unpack it:

# Linux
sha256sum -c SHA256SUMS --ignore-missing
tar -xzf kinetic-0.1.0-linux-x86_64-cuda.tar.gz
sudo install kinetic-0.1.0-linux-x86_64-cuda/kinetic /usr/local/bin/

# Windows (PowerShell): compare the hash with the line in SHA256SUMS
Get-FileHash .\kinetic-0.1.0-windows-x86_64-cuda.zip -Algorithm SHA256
Expand-Archive .\kinetic-0.1.0-windows-x86_64-cuda.zip -DestinationPath $env:LOCALAPPDATA\Programs

Then put the unpacked folder on your PATH and check:

kinetic --version

On Windows, SmartScreen may warn about a new download the first time you run it; see FAQ.

Python

pip install kinetic-engine

Wheels are built for Windows and Linux (manylinux 2.28) on x86-64, for Python 3.9 and newer. They run models on the CPU; to use an NVIDIA GPU from Python, build the package from source with the CUDA toolkit installed, or run kinetic serve and use any OpenAI client. See Python, C and Rust for the API.

Docker

The images run kinetic serve on port 8080 as an unprivileged user, with your models folder mounted read-only. Build them from a clone of the repository:

docker build -f docker/Dockerfile -t kinetic .
docker run --rm -p 8080:8080 -v "$PWD/models:/models:ro" kinetic /models/model.gguf

For NVIDIA GPUs, with the NVIDIA Container Toolkit on the host:

docker build -f docker/Dockerfile.cuda -t kinetic:cuda .
docker run --rm --gpus all -p 8080:8080 -v "$PWD/models:/models:ro" kinetic:cuda /models/model.gguf

Inside the container the server listens on all interfaces, so that -p 8080:8080 reaches it; -p 127.0.0.1:8080:8080 keeps it on your own machine. Arguments after the image name go to kinetic serve, so kinetic /models/model.gguf -c 8192 works as on the command line.

From source

With Rust 1.91 or newer:

git clone https://github.com/Veloresearch/kinetic
cargo install --path kinetic/crates/kinetic-cli

For GPU support, install the CUDA toolkit 12.8 or newer first. See Building from source.

Get a model

Any .gguf file of a supported architecture works; run, chat and serve only read files from your disk. Download one from Hugging Face with kinetic pull <org>/<repo> (see pull) or your browser. Models Ollama or LM Studio already downloaded run by name; see models. To try it out:

kinetic info model.gguf shows what a file contains and whether Kinetic supports it.