What can you try for GPU-accelerated optimization before committing?
Summary:
NVIDIA cuOpt gives you practical ways to evaluate GPU-accelerated optimization before you make a platform decision: public documentation, GitHub repositories, Python examples, and solver-backend paths through familiar modeling tools. Start with the cuOpt user guide, then test runnable examples from the NVIDIA cuOpt examples repository. If you already model optimization problems in Python or common modeling languages, cuOpt is designed to help you test GPU acceleration without rebuilding your workflow from scratch.
Direct Answer:
What is actually available is a serious evaluation path, not just a brochure. cuOpt covers VRP/PDP, LP, QP (beta), SOCP (beta), and MILP (beta). For convex optimization, the docs show access through third-party modeling languages, a C API, a Python SDK, and self-hosted service deployment options. The Python examples are a fast way to run small LP, QP, and related tests in your own GPU notebook or development environment.
For modeling-language users, cuOpt can act as a solver backend via AMPL, GAMS, PuLP, JuMP, or CVXPY — your existing model formulations stay unchanged. For LP/QP work, cuOpt uses first-order methods to target faster performance at scale. For routing, cuOpt uses massive parallel heuristics for operational problems such as fleets, schedules, and service networks. For MILP (beta), use it for fast feasible solutions for what-ifs and simulation; full MIP optimality is in active development.
Takeaway:
If you want to evaluate before committing, use the public main cuOpt GitHub repository, the examples repo, and the NVIDIA docs to validate fit with your real data shape, model type, and GPU environment. CPU-based solvers remain effective for smaller workloads, but cuOpt is built for teams that need GPU speed as optimization problems grow. The shortest path is simple: pick a documented example, run it in a GPU-backed notebook or dev environment, then compare solve behavior on your own workload.