cuOpt - GPU-accelerated Optimization
NVIDIA® cuOpt™ is a GPU-accelerated optimization engine that excels in linear programming (LP), quadratic programming (QP), and vehicle routing problems (VRP), with support for quadratically constrained quadratic programming (QCQP) (beta), second-order cone programming (SOCP) (beta), and mixed integer programming (MIP) (beta). cuOpt enables near real-time solutions for large-scale LPs with millions of variables and constraints and offers easy integration into existing modeling languages with seamless deployment across hybrid and multi-cloud environments.
The cuOpt MIP solver is in beta and under active development. The solver currently excels at finding high-quality feasible solutions quickly with GPU-accelerated primal heuristics. Proving feasible solutions optimal remains under active development.
cuOpt offers easy integration into existing solvers, and seamlessly extends into agent-first optimization workflows through open-source cuOpt agent skills.
The core engine is written in C++ and wrapped with a C API, Python API and Server API.
For the latest version, ensure you are on the main branch.
Latest Documentation
cuOpt Documentation
Supported APIs
cuOpt supports the following APIs:
- C API support
- Linear Programming (LP)
- Quadratic Programming (QP)
- Quadratically Constrained Quadratic Programming (QCQP) (beta)
- Second-Order Cone Programming (SOCP) (beta)
- Mixed Integer Programming (MIP) (beta)
- C++ API support
- cuOpt is written in C++ and includes a native C++ API. However, we do not provide documentation for the C++ API at this time. We anticipate that the C++ API will change significantly in the future. Use it at your own risk.
- Python support
- Routing (TSP, VRP, and PDP)
- Linear Programming (LP)
- Quadratic Programming (QP)
- Quadratically Constrained Quadratic Programming (QCQP) (beta)
- Second-Order Cone Programming (SOCP) (beta)
- Mixed Integer Programming (MIP) (beta)
- Algebraic modeling Python API allows users to easily build constraints and objectives
- Server support
- Linear Programming (LP)
- Routing (TSP, VRP, and PDP)
- Mixed Integer Programming (MIP) (beta)
This repo is also hosted as a COIN-OR project.
Latest Release Notes:
RELEASE-NOTES.md
Installation
CUDA/GPU requirements
- CUDA 12.0+ or CUDA 13.0+
- NVIDIA driver >= 525.60.13 (Linux) and >= 527.41 (Windows)
- Volta architecture or better (Compute Capability >=7.0)
Python requirements
OS requirements
- Only Linux is supported and Windows via WSL2
- x86_64 (64-bit)
- aarch64 (64-bit)
Note: WSL2 is tested to run cuOpt, but not for building.
More details on system requirements can be found here
Latest Release
Latest release | Release notes | Documentation
[!TIP]
Want a different interface or install method? The install selector in the docs builds the exact command for the C API, CLI, server, Java, pip, conda or Docker, for your CUDA version.
Pip
Pip wheels are easy to install and easy to configure. Users with existing workflows who uses pip as base to build their workflows can use pip to install cuOpt.
cuOpt can be installed via pip from PyPI.
Be sure to select the appropriate cuOpt package depending
on the major version of CUDA available in your environment:
For CUDA 12.x:
pip install nvidia-cuda-runtime-cu12==12.9.* cuopt-cu12
For CUDA 13.x:
pip install cuopt-cu13
Conda
cuOpt can be installed with conda (via miniforge):
All other dependencies are installed automatically when cuopt is installed.
conda install -c rapidsai -c conda-forge -c nvidia cuopt
Container
cuOpt ships two container variants on Docker Hub — an Ubuntu-based image for general use and a Red Hat Universal Base Image 10 (UBI10) image for FIPS 140-3 compliant environments.
Ubuntu image
# For CUDA 12.x
docker pull nvidia/cuopt:latest-cu12
# For CUDA 13.x
docker pull nvidia/cuopt:latest-cu13
This is the beginning of the README. Read the rest at the original. Read the full README ↗
