ROSE SOLVER

Massively parallel MIP solver. Delivered as a cloud service.

A high throughput, large scale solver for linear and mixed integer linear programming. Distributes the entire branch and bound process across CPUs, including branching, cuts, and heuristics. Consumed through a cloud native API and Python SDK, with pricing tied to the compute you actually use.

BUILT DISTRIBUTED

A modern solver, distributed by design.

Most commercial solvers were built decades ago for single machine workloads, with parallelism added later in narrow places. Rose Solver was designed from the ground up to be massively distributed. The engine is built on MPI for high end multi core compute nodes, with branch and bound, cuts, domain propagation, and a full set of primal heuristics all running natively across workers.

01

Distributed by default. Branching, cuts, and heuristics run in parallel from the start. The architecture is the parallelism, not a layer on top of a serial core.

02

Asynchronous, throughput first. The processing pipeline scales across orders of magnitude in nodes per second without assuming a fixed linear curve. As more compute is added, the engine is designed to continue exploiting it effectively, including cases where performance gains are super-linear.

03

Active research at the frontier. Pure parallel branch and bound has known limits. Every serious solver team is working past them. We are too, with research into next generation approaches that move beyond conventional parallelism.

SEAMLESS ACCESS
A differentiated delivery model.
If you’re evaluating optimization solvers today, you’re choosing between commercial license, open source, and managed cloud service. Here’s how Rose Solver compares.

Leading Commercial Solver

Leading Open Source Solver

Rose Solver

Delivery model

Software license, on premises or cloud VM

Open source library, self hosted

Cloud service, REST API and Python SDK

Pricing

Per seat or per machine licenses

Free

Consumption based, pay per solve

Infrastructure

Customer managed (or pays per machine hour on cloud)

Customer managed

Fully managed

Parallelism

Limited parallelism

Limited parallelism

Fully distributed branch and bound tree

Speed across general MIP

Industry leading on most benchmarks

Slower than commercial solvers

Better than open source on most benchmarks; commercially competitive on decomposable problems

Time to first solve

Procure, install, configure, tune

Download, install, configure, tune

API call

PROBLEM CLASSES

Strong across MIP. Concentrated where it matters most.

Rose performs well across a broad range of MIP and MIPLIB problems:

  • Network design and capacity planning. Topology optimization, capacity allocation, and routing across communication, energy, and distribution networks. Common in telecom backbone design, utility infrastructure planning, and content delivery networks.
  • Transportation and supply chain allocation. Distribution planning, shipping allocation, and bipartite matching at enterprise scale. Common in multi-warehouse fulfillment, freight optimization, and supplier-to-plant flow planning.
  • Vehicle routing and fleet logistics. Last-mile delivery, field service, and large-scale fleet routing with real-world constraints — time windows, vehicle capacity, driver schedules, and mixed vehicle types. GPU-accelerated for problem sizes that stall traditional solvers.
  • Stochastic and scenario optimization. Multi-scenario planning under uncertainty where parallel evaluation across scenarios is a natural fit for our architecture. Increasingly critical for supply chain resilience, energy planning, and risk-driven decision making.
  • Coverage, assignment, and resource allocation. Set covering, binary assignment, and capacity-constrained allocation problems with rich combinatorial structure. Common in airline crew planning, retail allocation, defense coverage, and facility location.
  • Large-scale linear programming. Continuous optimization at scale — energy dispatch, production planning, and large network flow problems where solve time, reproducibility, and integration matter as much as raw speed.
  • Multi-mode scheduling and resource-constrained planning. Project portfolio scheduling, manufacturing, and integrated planning where multiple resource types and operational modes must be coordinated together.

DELIVERY MODEL

Cloud native by default. Consumed like any cloud service.

No install, no infrastructure to provision, no software to license. Rose Solver is delivered through a cloud native REST API and a Python SDK, with consumption based pricing tied to the compute time you actually use.

Time limits are configurable, putting you in control of both the optimality gap and the bill.

REST API + Python SDK

Direct integration into any application, AI agent, or workflow.

Consumption pricing

Pay only for active solve time. No per seat licensing, no per machine fees.

Transparent usage

Set time limits and optimality gaps to control spend.

Zero infrastructure

No GPUs to procure, no Docker to configure, no clusters to maintain.

ADJACENT PRODUCT · NVIDIA · PARTNER

Have a routing problem instead?Look at Rose cuOpt.

Rose Solver is built for general LP and MIP. For large scale vehicle routing problems, we offer Rose cuOpt: managed access to NVIDIA cuOpt running on our GPU infrastructure. Same cloud native delivery, same consumption based pricing, purpose built for routing and logistics teams.

FREQUENTLY ASKED
Common questions about Rose Solver.

Linear programming and mixed integer linear programming. Rose performs well across a broad range of MIP and MIPLIB problems, with concentrated investment in network design, stochastic and scenario problems, transportation and supply chain allocation, and coverage and assignment.

Two things. Architecturally, Rose distributes the entire branch and bound process across CPUs rather than parallelizing only narrow steps. Commercially, Rose is a cloud service with consumption pricing rather than a software license you install on your own infrastructure.

Consumption based. You pay for active solve time, not per seat or per machine. Beta pricing details are shared on request.

No. Rose Solver is delivered as a cloud service. You call it through a REST API or our Python SDK. There is no software to install, no infrastructure to provision, and no license to manage.

Python is supported today, with additional languages on the roadmap. The REST API itself is language agnostic and can be called from any environment that can make HTTPS requests.

Yes, if you have a MIP formulation of your routing problem, Rose Solver will run it. For teams that want to try a GPU accelerated alternative purpose built for large scale vehicle routing, we also offer Rose cuOpt, which gives you managed access to NVIDIA cuOpt. Both products use the same cloud delivery model and consumption pricing.

Join the beta program and submit your own MPS or LP files through the API. You can also request a benchmark, in which case our team runs your instance and walks you through the results. Both paths are open.ext step on the product roadmap. Early access users will be first to know when that capability is available

GET STARTED
Want to run Rose Solver on your own data?

Or request a benchmark and our team will run it with you.