Private AI shouldn't come with an outrageous infrastructure bill.
Enterprises want more control over their AI. They want to choose the models they use, keep sensitive data inside environments they control, and reduce dependence on a handful of commercial AI providers.
Open-weight models are making that possible. But there's a problem.
Running private AI can be expensive. Really expensive.
For years, the industry's answer has been more hardware. Bigger GPU clusters. More specialized infrastructure. More cloud capacity. More money. At Rival, we think there's a better way.
Today, we're introducing GraphX, Rival's proprietary GPU proprietary kernel-optimization and silicon-placement technology designed to reduce open-weight AI model hosting costs by 50-95% or more.
GraphX tackles the cost problem from two directions: making models run more efficiently on fewer GPUs and finding lower-cost infrastructure to run them.
The result? More AI. Less compute. Lower costs.
Private AI Has an Economics Problem
Open-weight models have changed what's possible for enterprises. Organizations no longer have to send every AI workload to a commercial model provider. They can choose from a growing ecosystem of powerful models, customize them for specific use cases, and deploy them inside environments they control.
That creates enormous opportunities for privacy, flexibility, and independence. But control comes with an infrastructure burden.
Large AI models can require significant GPU resources. When workloads are distributed across multiple GPUs, those processors need to communicate with one another. That communication takes time and resources. GPUs can spend valuable cycles waiting for information instead of performing useful computation.
Think of it like hiring a team of incredibly productive people, then designing a process that requires them to constantly stop working and wait for one another. Adding more people doesn't necessarily fix the process.
Adding more GPUs doesn't necessarily fix AI infrastructure, either. And even when organizations have the right hardware configuration, they may be paying far more than necessary to host it.
That's the problem GraphX was built to solve.
Use Less Compute. Pay Less for Compute.
Most companies don’t have a model problem. They have a cost and silicon problem. GraphX addresses both.
01 / Use Less Compute
When AI performance falls short, adding more hardware is often the default solution. Rather than throwing more hardware at AI performance problems, GraphX improves how models run on the hardware available.
It optimizes how open-weight models execute across available GPU resources, reducing unnecessary communication between GPUs and making better use of the computing power already available.
Through smarter model placement, GraphX can enable supported workloads to run on fewer GPUs while meeting performance requirements.
No quantization. No compression. Just smarter execution.
Running powerful AI models on fewer GPUs doesn't have to mean compromising model quality. GraphX’s placement optimization technology makes more efficient use of available hardware, reducing GPU requirements without relying on model compression, degradation, or quantization. The result is lower infrastructure costs without sacrificing the model’s original precision
02 / Pay Less for Compute
Using fewer GPUs is only half the equation. Where those GPUs run matters just as much.
GPU pricing can vary significantly across hyperscale cloud platforms and specialized AI infrastructure providers, often called neoclouds.
GraphX includes Silicon Finder, Rival's patent-pending technology that continuously searches live GPU and accelerator availability across a growing network of providers.
It identifies lower-cost compatible capacity and enables supported models to be deployed on demand.
Instead of being locked into a single provider or expensive hardware configuration, enterprises gain more flexibility in where their AI runs.
Use fewer GPUs. Find better prices. Reduce the bill.
Know the Cost Before You Deploy
Enterprises shouldn't have to commit to expensive infrastructure just to find out what running a model will cost.
That's why we're also introducing the GraphX Cost Calculator.
The calculator compares estimated hosting costs across major cloud platforms and neocloud providers, with and without GraphX optimization.
Organizations can explore compatible configurations, understand their infrastructure options, and estimate potential savings before deploying.
Calculate Your AI Savings →
Private AI Without the Traditional Infrastructure Burden
Cost is only one reason enterprises are rethinking how they run AI. Control matters, too. Organizations want to protect sensitive information, choose the models best suited to their needs, and avoid becoming dependent on a single AI or infrastructure provider. GraphX supports that flexibility.
Enterprises can deploy supported models in their own cloud, their own data center, or on dedicated Rival infrastructure. Each deployment runs in a dedicated, isolated environment with its own secure endpoint and access credentials, restricting connections to authorized users and applications.
Those models can then power workflows, agents, and applications inside Rival or connect to third-party tools. Organizations can start with a single workload, expand what works, and pause or shut down deployments as requirements change.
The goal isn't simply cheaper AI. It's more affordable AI without giving up control.
The Infrastructure Behind Rival's AI Economics
GraphX is part of a bigger idea at Rival. We believe businesses shouldn't have to manage the complexity underneath AI to benefit from it.
A marketer shouldn't need to understand GPU architecture to run an AI workflow. A business team shouldn't have to decide which cloud provider offers the best hardware for every task. And developers shouldn't have to constantly worry about the cost of every experiment.
Rival is building intelligence and efficiency into each layer of the AI stack.
CortexOne routes workloads to the best-fit infrastructure based on performance, cost, and business requirements.
GraphX optimizes how models execute on that infrastructure while helping identify lower-cost compatible GPU capacity.
Together, these technologies help organizations make smarter decisions about what AI runs, where it runs, and what it costs. Above those infrastructure layers, Rival provides one platform to build, run, govern, and reuse AI across the enterprise. The infrastructure becomes smarter so the experience becomes simpler.
It's also part of what makes Rival's All-You-Can-Eat approach possible. By reducing underlying infrastructure costs, Rival can offer unlimited DeepSeek 4 Flash model usage without token charges, while billing separately for compute and optional paid Marketplace tools.
The Future Isn't Just More Compute. It's Smarter Compute.
The first era of generative AI has been defined by scale. More parameters. More data. More GPUs. More compute. And more spending. Scale will continue to matter. But the next phase of AI infrastructure can't simply be an endless race to consume more resources.
Enterprises need AI that delivers real business value without an infrastructure bill that undermines the return. That requires a different approach.
Use less compute. Find better compute. Make every GPU dollar go further.
That's what we're building with GraphX.
Ready to Rethink Your AI Costs?
See what GraphX could save your organization.
Calculate Your AI Savings →