Simulating AI Infrastructure Before It Is Built: BE Networks, IREN and NVIDIA DSX Air
Large-scale AI infrastructure has entered a new phase.
The industry is no longer asking whether organizations can acquire GPUs. The more important question is whether they can deploy, connect, validate and operate massive GPU clusters with the precision required for production AI workloads.
That is why BE Networks is working with IREN to use NVIDIA DSX Air to simulate and validate the network architecture supporting IREN’s upcoming deployment of more than 50,000 NVIDIA GPUs.
For AI cloud providers, this represents a meaningful shift in how large-scale infrastructure is delivered. Instead of waiting for physical hardware to arrive before validating network behavior, automation workflows and operational readiness, teams can now model the environment in advance using a production-representative digital twin.
Why simulation matters at 50,000+ GPU scale
At GPU cluster scale, infrastructure performance depends on much more than raw compute capacity.
The network fabric is a critical determinant of GPU utilization, workload efficiency, training performance, inference throughput and customer time-to-compute. Every layer matters: physical topology, routing behavior, congestion management, provisioning accuracy, security policy, operational telemetry and change control.
In smaller environments, teams can often rely on manual validation, staged testing or traditional lab environments. At 50,000+ GPU scale, that approach is not enough.
A single design issue, misconfiguration, automation defect or untested operational process can delay production readiness, introduce risk across thousands of interconnected systems, or reduce the effective value of high-performance GPU infrastructure.
Simulation changes that operating model.
With NVIDIA DSX Air, infrastructure teams can model large-scale AI environments before deployment. They can validate network topologies, rehearse provisioning workflows, test operational procedures and identify failure scenarios before they affect production systems.
Building AI factories at massive scale demands a new level of precision and confidence in the underlying network fabric and infrastructure design. Leveraging NVIDIA DSX Air with BE Networks’ automation capabilities, IREN can shift critical validation earlier in the lifecycle to accelerate the path to customer capacity.
From digital twin to production-ready infrastructure
The value of simulation is not limited to pre-deployment testing. Its real power comes from connecting simulation to automation.
That is where BE Networks’ Verity platform plays an important role.
Verity helps infrastructure teams translate validated design intent into repeatable deployment and operational workflows across the full network lifecycle. This includes Day 0 design, Day 1 turn-up and Day 2 operations. That means that for a fabric of 50,000+ GPUs, BE saves thousands of engineering hours and reduces time to first token from the network perspective by more than 90%.
For a deployment of this scale, that lifecycle discipline is essential.
Before the physical environment is deployed, teams can use NVIDIA DSX Air to simulate critical elements of the AI cloud fabric. They can validate expected behavior, test configuration logic, confirm automation outcomes and rehearse changes. Verity can then help carry that validated intent into production through consistent automation and orchestration.
The result is a more disciplined deployment model:
Reducing risk before production
AI infrastructure is capital intensive, schedule sensitive and operationally complex. The cost of delay is high. The cost of poor utilization is higher.
For organizations building large-scale AI cloud platforms, reducing deployment risk is a strategic priority. Network validation can no longer be treated as a late-stage activity that happens after the hardware is already installed. It must become part of the planning and readiness process from the beginning.
By using NVIDIA DSX Air with BE Networks’ automation capabilities, IREN can shift critical validation earlier in the lifecycle. This helps reduce integration risk, improve operational readiness and accelerate the path to customer capacity.
That approach is especially important as AI cloud environments become larger, denser and more distributed. The complexity of these systems makes it difficult to rely on manual processes or reactive troubleshooting. Modern AI infrastructure requires predictive validation, closed-loop automation and a consistent operational model that scales with the environment.
A new standard for AI cloud deployment
The collaboration between IREN, BE Networks and NVIDIA DSX Air reflects a broader change in how AI infrastructure will be designed and operated.
The next generation of AI factories will not be built solely through physical deployment and post-installation troubleshooting. They will be modeled, simulated and validated in advance. Their network fabrics will be tested before they go live. Their automation workflows will be rehearsed before they touch production. Their operational procedures will be refined before customer workloads depend on them.
This is the direction the industry is moving.
For BE Networks, the opportunity is clear: help customers move from infrastructure complexity to infrastructure confidence.
By combining digital twin simulation with intent-based automation, BE Networks is helping organizations build a more reliable path from AI infrastructure design to production-scale operation.
For IREN’s upcoming 50,000+ GPU deployment, that means validating one of the most complex layers of AI cloud infrastructure before it is physically deployed.
For the broader market, it signals what the future of AI infrastructure delivery will look like.
Josh Saul
Senior Vice President – Product
Josh Saul has pioneered open source network solutions for more than 25 years. As an architect, he built core networks for GE, Pfizer and NBC Universal. As an engineer at Cisco, Josh advised customers in the Fortune 100 financial sector and evangelized new technologies to customers. More recently, Josh led marketing and product teams at VMware (acquired by Broadcom), Cumulus Networks (acquired by NVIDIA), and Apstra (acquired by Juniper).
