**Decision Optimization** # NVIDIA cuOpt Achieve world-record speed on large-scale problems with millions of constraints and variables—saving time and reducing costs. [Get Started](https://www.nvidia.com/en-us/ai-data-science/products/cuopt/get-started.md) [Watch Video](https://www.youtube.com/watch?v=a9O0JipIrb4) | [Blog](https://developer.nvidia.com/blog/accelerate-decision-optimization-using-open-source-nvidia-cuopt/) | [For Developers](https://github.com/NVIDIA/cuopt) ## Overview ## What Is NVIDIA cuOpt? NVIDIA® cuOpt™ is an open source, GPU-accelerated engine for decision optimization, excelling in [mixed-integer programming](https://www.nvidia.com/en-us/glossary/mixed-integer-programming.md) (MIP), [linear programming](https://developer.nvidia.com/blog/accelerate-large-linear-programming-problems-with-nvidia-cuopt/#cuopt_outperforms_state-of-the-art_cpu_lp_solvers_on_mittelmann%E2%80%99s_benchmark) (LP), [vehicle routing problems](https://docs.nvidia.com/cuopt/user-guide/latest/introduction.html#routing-tsp-vrp-and-pdp) (VRPs), and [quadratic programming](https://docs.nvidia.com/cuopt/user-guide/latest/lp-qp-features.html#quadratic-programming) (QP). Designed to tackle large-scale problems with millions of variables and constraints, cuOpt enables accelerated decision-making. ### Accelerate MIP Solvers Using Primal Heuristics Solve large, latency-sensitive MIP problems faster with GPU-accelerated primal heuristics that deliver high-quality feasible solutions beyond what traditional CPU solvers can achieve. [Read Blog](https://developer.nvidia.com/blog/learn-how-nvidia-cuopt-accelerates-mixed-integer-optimization-using-primal-heuristics/) ### Introducing NVIDIA cuOpt’s Barrier Method Linear Programming Solver A GPU-accelerated barrier method delivering fast, accurate solutions at scale, with significant speedups over the leading open-source CPU solver. [Read Blog](https://developer.nvidia.com/blog/solve-linear-programs-using-the-gpu-accelerated-barrier-method-in-nvidia-cuopt/) ### Benefits ## Explore the Benefits of NVIDIA cuOpt ### GPU-Powered Speedup Enjoy [significant speedups over CPU LP solvers](https://developer.nvidia.com/blog/accelerate-large-linear-programming-problems-with-nvidia-cuopt/#cuopt_outperforms_state-of-the-art_cpu_lp_solvers_on_mittelmann%E2%80%99s_benchmark) when lower-accuracy solutions are acceptable. Outperform commercial state-of-the-art VRP solvers. ### World-Record Solutions Achieve a world-record solution validated on the [MIPLIB](https://miplib.zib.de/instance_details_bts4-cta.html#:~:text=Best%20Known%20Solution(s)) open problem, competitive [performance on large LPs](https://developer.nvidia.com/blog/accelerate-large-linear-programming-problems-with-nvidia-cuopt/#cuopt_outperforms_state-of-the-art_cpu_lp_solvers_on_mittelmann%E2%80%99s_benchmark) demonstrated by the [Mittelmann](https://plato.asu.edu/ftp/lpfeas.html) benchmarks, and [unmatched precision for VRP](https://resources.nvidia.com/en-us-cuopt/world-record-blog?xs=492752), validated by the [Gehring & Homberger](https://www.sintef.no/projectweb/top/vrptw/homberger-benchmark/1000-customers/) and [Li & Lim](https://www.sintef.no/projectweb/top/pdptw/li-lim-benchmark/) benchmarks. ### Seamless Scalability Effortlessly scale across hybrid and multi-cloud environments—while accelerating existing [AMPL](https://ampl.com/), [CVXPY](https://www.cvxpy.org/), [PuLP,](https://coin-or.github.io/pulp/) [Pyomo](https://www.pyomo.org/), and [SciPy](https://scipy.org/) models with zero-code integration. ### Dynamic and Batch Optimization Continuously adapt to changing variables and constraints by rerunning models in near real time or batch mode for optimal decision-making. ### Stand-Alone or Integrated Use out of the box or seamlessly embed into your solver for unmatched speed, scalability, and accuracy. ### Enterprise Support Accelerate time to value with the security, reliability, and enterprise-class support of [NVIDIA AI Enterprise](https://www.nvidia.com/en-us/data-center/products/ai-enterprise.md) for production deployments. Use Cases ## How cuOpt Is Being Used Explore how NVIDIA cuOpt powers real-world industry use cases, and jump-start your AI development with curated examples. 1. Supply Chain Management 2. Fleet Management 3. Last-Mile Delivery 4. Field Dispatch 5. Job Scheduling Optimization 6. Portfolio Optimization ### Supply Chain Management Optimizing resource allocation in complex supply chains requires efficiently distributing limited resources while adapting to real-time changes. With countless variables at play, achieving maximum productivity and cost efficiency demands rapid, intelligent decision-making. NVIDIA’s cuOpt-powered AI agent enables you to talk to your supply chain data via [NVIDIA NIM™](https://build.nvidia.com/search?q=LLM), delivering real-time, optimal resource allocation for greater operational agility and optimizing your resource allocation. [Read How to Build an AI Agent for Supply Chain Optimization](https://developer.nvidia.com/blog/building-an-ai-agent-for-supply-chain-optimization-with-nvidia-nim-and-cuopt/) [See How cuOpt Transforms Supply Chain Optimization](https://www.youtube.com/watch?v=a9O0JipIrb4) ### Fleet Management Efficient scheduling and route planning are essential for managing inbound and outbound transportation of goods and vehicles, especially for long-haul fleets. NVIDIA cuOpt, integrated with [Omniverse™ Digital Twins](https://www.nvidia.com/en-us/omniverse/solutions/digital-twins.md), optimizes logistics by simulating real-world fleet operations in a virtual environment, enabling dynamic scheduling, route optimization, and predictive planning. By factoring in the availability of pilots, drivers, and ships, cuOpt enhances decision-making with real-time insights, reducing transit times, improving resource utilization, and enhancing overall operational efficiency. [Read How SyncTwin Optimizes Intralogistics for Its Customers](https://developer.nvidia.com/blog/transforming-microsoft-xls-and-ppt-files-into-a-factory-digital-twin-with-openusd/#optimizing_intralogistics_with_cuopt) [Watch How BMW Optimizes Logistics With ipolog, NVIDIA cuOpt, and Omniverse](https://resources.nvidia.com/en-us-ai-optimization-content/gtcfall22-a41385) ### Last-Mile Delivery Efficiently dispatching truck fleets from distribution centers to retail stores and end customers is critical for minimizing costs and meeting delivery expectations. NVIDIA cuOpt optimizes route planning in real time, reducing miles driven, cutting delivery time, and lowering fuel consumption—ultimately decreasing operational costs and reducing pollution for more sustainable last-mile logistics. [Read How to Use Azure Maps and NVIDIA cuOpt for Multi-Itinerary Optimization](https://www.microsoft.com/en-us/maps/news/enhancing-logistics-with-azure-maps-and-nvidia-cuopt-for-multi-itinerary-optimization) ### Field Dispatch Effective field dispatch ensures service providers complete scheduled tasks efficiently while accounting for varying job durations and logistical challenges. For example, a telecommunications technician may need to install a router at one location and set up a data cable at another—each requiring different tools, time, and travel routes. NVIDIA cuOpt optimizes route planning and scheduling, ensuring technicians are fully prepared before departure and follow the most efficient route. This minimizes travel time, maximizes productivity, and enhances service quality, leading to improved customer satisfaction. ### Job Scheduling Optimization Job scheduling is the process of assigning tasks or jobs to available resources—such as machines, workers, or networks—over time to optimize a specific objective, such as minimizing costs and delays, or maximizing efficiency and throughput. With GPU acceleration, NVIDIA cuOpt enables businesses to make data-driven scheduling decisions, improving operational efficiency and responsiveness in fast-changing environments. ### Portfolio Optimization Effective stock allocation in finance requires strategically distributed investment capital across securities while balancing risk, return, and market dynamics. Investors must navigate volatility, economic indicators, and individual preferences, [making real-time adjustments to optimize portfolio performance](https://developer.nvidia.com/blog/accelerating-real-time-financial-decisions-with-quantitative-portfolio-optimization/). The challenge lies in evaluating countless possible combinations and rapidly adapting to shifting market conditions to maintain a competitive edge. [Watch How to Accelerate Portfolio Optimization and Boost Investment Performance](https://www.nvidia.com/en-us/on-demand/session/gtc25-dlit71690/) [Try the Quantitative Portfolio Optimization Developer Example](https://build.nvidia.com/nvidia/quantitative-portfolio-optimization) ### Starting Options ## Ways to Get Started With NVIDIA cuOpt Streamline your optimization problems from data to decisions. Try ### Google Colab Experience NVIDIA cuOpt on Google Colab for GPU-accelerated decision optimization for a diverse set of use cases available for rapid exploration and experimentation. [Try Now](https://colab.research.google.com/github/NVIDIA/cuopt-examples/blob/cuopt_examples_launcher/cuopt_examples_launcher.ipynb) Try ### NVIDIA API Catalog Experience NVIDIA cuOpt for accelerated decision optimization of an interactive vehicle routing problem (VRP) example through an API interface. [Try Now](https://build.nvidia.com/nvidia/nvidia-cuopt) Develop ### GitHub NVIDIA cuOpt  is available as open-source software on [GitHub](https://github.com/NVIDIA/cuopt), [PIP](https://github.com/NVIDIA/cuopt/blob/branch-25.10/README.md), [Docker](https://hub.docker.com/r/nvidia/cuopt), [Conda](https://anaconda.org/rapidsai-nightly/cuopt-server), and [NVIDIA NGC™](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/cuopt/containers/cuopt?version=25.8.0-cuda12.8-py3.12). Also available via third-party integration: [AMPL](https://dev.ampl.com/solvers/cuopt/index.html), [CVXPY](https://www.cvxpy.org/install/), [PuLP](https://coin-or.github.io/pulp/), [GAMSPy](https://www.gams.com/blog/2025/09/gpu-accelerated-optimization-with-gams-and-nvidia-cuopt/), and [JuMP](https://jump.dev/JuMP.jl/stable/packages/cuOpt/). [Access Code Repo](https://github.com/NVIDIA/cuopt) Deploy ### NVIDIA AI Enterprise Get support for cuOpt with [NVIDIA AI Enterprise](https://www.nvidia.com/en-us/data-center/products/ai-enterprise.md). [Learn More](https://www.nvidia.com/en-us/data-center/products/ai-enterprise.md) [Compare Ways to Get Started](https://www.nvidia.com/en-us/ai-data-science/products/cuopt/get-started.md) ### Customer Stories ## How Industry Leaders Are Innovating With cuOpt [More Customer Stories](https://www.nvidia.com/en-us/case-studies.md) ### Lowe’s Transforms Supply Chain Operations With Palantir Ontology and NVIDIA AI Lowe’s manages its massive supply chain of 7,500 vendors, 130 distribution centers, and 1,700+ stores using AI-powered technology from Palantir and NVIDIA. When disruptions like weather delays occur, intelligent agents use NVIDIA cuOpt to automatically re-optimize shipping routes and allocate resources in real time to maintain seamless operations. [Watch Video](https://www.youtube.com/watch?v=AsnE98temZo) ### Resources ## The Latest in NVIDIA cuOpt Resources 1. Blogs 2. Sessions 3. Training 4. Videos Load More [View All Blogs](https://blogs.nvidia.com/blog/tag/nvidia-cuopt/) [View More Sessions](https://www.nvidia.com/en-us/on-demand/search/?facet.mimetype%5B%5D=event%20session&layout=list&page=1&q=cuopt&sort=relevance&sortDir=desc) ### Accelerating Portfolio Optimization Learn to accelerate portfolio optimization with GPUs, optimize risk-reward tradeoffs, and transform algorithms for parallel processing with cuOpt. Explore real-world examples and compare CPU versus GPU performance for financial applications. [Watch On-Demand Session](https://www.nvidia.com/en-us/on-demand/session/gtc25-dlit71690/) ### Use a Route Optimization Cloud Service to Drive Efficiency and Cost Savings In this hands-on lab, learn how to use the NVIDIA cuOpt cloud service to optimize routes for diverse vehicle fleets and improve deliveries, pickups, job dispatching, and overall logistics efficiency. [Watch On-Demand Session](https://resources.nvidia.com/en-us-ai-optimization-content/gtc24-dlit62051) ### World-Record Route Optimization With NVIDIA cuOpt Discover how organizations achieve greater efficiency, cost savings, and enhanced customer satisfaction through real-time route optimization. [Watch Now](https://resources.nvidia.com/en-us-ai-optimization-content/cuopt-demo?lx=wujJKU) ### Fusing Real-Time AI With Digital Twins Learn how NVIDIA [Metropolis](https://www.nvidia.com/en-us/autonomous-machines/intelligent-video-analytics-platform.md), [Omniverse™](https://www.nvidia.com/en-us/omniverse.md), cuOpt, and [Isaac™](https://developer.nvidia.com/isaac) enable end-to-end automation of complex co-bot spaces, revolutionizing logistics with real-time AI and digital twins. [Watch Now](https://resources.nvidia.com/en-us-ai-optimization-content/real-time-ai-with-digital-twins) ### Talk to Your Supply Chain Data See how organizations overcome operations complexities and scale AI-driven factories using an AI planner powered by [LLM NIM microservices](https://build.nvidia.com/search?q=LLM), [NeMo™ Retriever NIM microservices](https://build.nvidia.com/explore/retrieval), and [cuOpt](https://build.nvidia.com/search?q=cuOpt). [Watch Now](https://www.youtube.com/watch?v=a9O0JipIrb4) [View More Videos](https://resources.nvidia.com/l/en-us-ai-optimization-content/?ncid=no-ncid&contentType=video) Next Steps ## Ready to Get Started? Use the right tools and technologies to take logistics optimization projects from development to production. [Get Started](https://www.nvidia.com/en-us/ai-data-science/products/cuopt/get-started.md) ### For Developers Explore everything you need to start developing with NVIDIA cuOpt, including the latest documentation, tutorials, technical blogs, and more. [Start Developing](https://github.com/NVIDIA/cuopt) ### Get in Touch Talk to an NVIDIA product specialist about moving from pilot to production with the security, API stability, and support of [NVIDIA AI Enterprise](https://www.nvidia.com/en-us/data-center/products/ai-enterprise.md). [Contact Us](https://www.nvidia.com/en-us/data-center/products/ai-enterprise/contact-sales.md) ### Domino’s Pizza #### Vehicle Routing at Domino’s: Exploring a GPU-Enabled Approach [Domino’s Pizza](https://www.dominos.com/en/) delivers thousands of pizzas a day and requires real-time planning and logistics capabilities. Domino’s has implemented a real-time planning system that fits its strict requirements and delivers sub-second runtimes for its use case. [Learn More](https://www.nvidia.com/en-us/on-demand/session/gtcfall21-a31074/) ### Kawasaki #### Reinventing Maintenance Operations With cuOpt and Jetson Orin [Kawasaki Heavy Industries](https://global.kawasaki.com/), Ltd. (Kawasaki) is a manufacturing company that’s been building large machinery for more than a hundred years. With [NVIDIA cuOpt](https://www.nvidia.com/en-us/ai-data-science/products/cuopt.md) and [NVIDIA Jetson™ Orin](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/nano-super-developer-kit.md), Kawasaki partnered with [Slalom, Inc.](https://www.slalom.com/us/en) to transform its track maintenance and inspection capabilities. [Learn More](https://www.nvidia.com/en-us/case-studies/reinventing-maintenance-operations-with-ai.md) ### Shell #### Shell Optimizes Energy Markets With AI-Powered Simulations [Shell](https://www.shell.com/) is integrating [NVIDIA cuOpt](https://www.nvidia.com/en-us/ai-data-science/products/cuopt.md) to batch optimizations across multiple simulations, addressing unpredictable control issues in power and gas markets. Now, Shell has the ability to make competitive bids, while simultaneously reducing costs and improving efficiency in energy infrastructure; this innovation supports the global transition to lower-carbon energy. ### AMPL #### AMPL Accelerates Electricity Market Optimization With NVIDIA cuOpt [AMPL](https://ampl.com/), an industry-leading modeling system built specifically for large-scale optimization, has seamlessly integrated [NVIDIA cuOpt](https://www.nvidia.com/en-us/ai-data-science/products/cuopt.md) to revolutionize optimization. By harnessing GPU-accelerated computing, AMPL has reduced problem-solving times from 2 minutes to just 2-3 seconds, significantly enhancing efficiency, scalability, and real-time decision-making in performance-critical energy applications. [Learn More](https://ampl.com/blog/breaking-barriers-in-optimization-ampls-early-results-with-nvidia-cuopt) ### Blue Yonder #### Blue Yonder Accelerates Last-Mile Delivery With cuOpt [Blue Yonder](https://blueyonder.com/) is transforming supply chain planning and management with AI-powered solutions. Accelerated by [NVIDIA cuOpt](https://www.nvidia.com/en-us/ai-data-science/products/cuopt.md), its end-to-end supply chain solution platform optimizes last-mile delivery, enabling thousands of deliveries daily across hundreds of vehicles with greater efficiency. ### Deloitte #### Deloitte Unveils Suite of AI Service Offerings Built on NVIDIA Platforms [Deloitte](https://www2.deloitte.com/us/en.html) [Compass AI](https://www2.deloitte.com/us/en/pages/consulting/solutions/nvidia-alliance-accelerated-ai.html), powered by [NVIDIA cuOpt](https://www.nvidia.com/en-us/ai-data-science/products/cuopt.md), revolutionizes fleet routing and dispatch optimization by embedding AI directly into workflows. By rapidly processing data and simulating scenarios in seconds, Compass AI enables organizations to reduce costs, improve delivery speed, and enhance customer satisfaction. Announced alongside Deloitte recognition as NVIDIA Consulting Partner of the Year, Compass AI exemplifies cutting-edge AI-driven logistics solutions. [Learn More](https://www2.deloitte.com/content/dam/Deloitte/us/Documents/consulting/us-nvidia-quartzcompassai.pdf) ### EY #### Revolutionize Supply Chain Analytics With AI and Accelerated Computing [EY](https://www.ey.com/en_us) Supply Chain & Operations Platform (SC&OP) leverages [NVIDIA cuOpt](https://www.nvidia.com/en-us/ai-data-science/products/cuopt.md) and [NIM-powered AI agents](https://www.nvidia.com/en-us/ai.md) to optimize supply chain decision-making in seconds instead of hours. By integrating advanced solvers ([Heuristics, MIP, LP](https://docs.nvidia.com/cuopt/user-guide/latest/introduction.html#the-necessity-for-heuristics)), SC&OP orchestrates logistics, manufacturing, and sourcing with AI-driven precision—boosting efficiency, agility, and resilience while driving measurable [EBITDA](https://en.wikipedia.org/wiki/Earnings_before_interest,_taxes,_depreciation_and_amortization) improvements. [Learn More](https://www.nvidia.com/en-us/on-demand/session/gtc25-s74122/) ### Lyric #### AI-Driven Decision Intelligence for the Modern Supply Chain [Lyric](https://lyric.tech/) is the first enterprise AI platform for supply chain decision intelligence, empowering organizations to design, plan, and operate with unprecedented agility. By integrating data, algorithms, workflows, and user experiences, Lyric delivers dynamic, AI-driven solutions. As an NVIDIA partner, Lyric leverages [NVIDIA cuOpt](https://www.nvidia.com/en-us/ai-data-science/products/cuopt.md) to accelerate distribution optimization for major consumer packaged goods (CPG) companies, cutting routing times from 4 hours to just 2 minutes—a 120X speedup—while improving solution quality by 200 basis points. Lyric and NVIDIA cuOpt continue to expand GPU acceleration in supply chain decision-making, boosting efficiency, resilience, and competitive edge at scale. [Learn More](https://lyric.tech/blog/lyric-leverages-nvidia-cuopt-to-elevate-supply-chain-ai) ### Microsoft #### Enhancing Logistics With Azure Maps and NVIDIA cuOpt for Multi-Itinerary Optimization At its core, effective route optimization requires reliable inputs to estimate travel times and the ability to apply key constraints such as driver availability, service duration, operating hours, demand, and capacity. In the solution, [Azure Maps](https://azure.microsoft.com/en-us/products/azure-maps) supplies the essential routing data, while NVIDIA cuOpt processes these constraints to deliver optimized, real-time scheduling and logistics efficiency. [Learn More](https://www.microsoft.com/en-us/maps/news/enhancing-logistics-with-azure-maps-and-nvidia-cuopt-for-multi-itinerary-optimization) ### SimpleRose #### Turbocharging Prescriptive Analytics and Optimization With GPU Acceleration As industries face increasing demands in logistics, scheduling, and portfolio management, traditional optimization approaches struggle to scale efficiently. [SimpleRose](https://simplerose.com/) integrates [NVIDIA cuOpt](https://www.nvidia.com/en-us/ai-data-science/products/cuopt.md) to accelerate linear programming (LP) and mixed-integer linear programming (MILP), delivering significant speedups without compromising accuracy. [Learn More](https://simplerose.com/blog/how-simplerose-and-nvidia-cuopt-solve-lp-and-milp-problems-faster) ### Slalom #### Reinventing Maintenance Operations With cuOpt and Jetson Orin [Slalom, Inc.](https://www.slalom.com/us/en) partnered with [Kawasaki Heavy Industries, Ltd.](https://global.kawasaki.com/) to transform track maintenance and inspection using [NVIDIA cuOpt](https://www.nvidia.com/en-us/ai-data-science/products/cuopt.md) and [NVIDIA Jetson™ Orin](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/nano-super-developer-kit.md). With over a century of experience in manufacturing large machinery, Kawasaki leveraged Slalom’s experience to enhance operational efficiency in maintenance processes. [Learn More](https://www.nvidia.com/en-us/case-studies/reinventing-maintenance-operations-with-ai.md)