Why AI Factory Networking Should Be Validated Before Deployment


Assembling AI infrastructure is significantly different than just installing servers and switches. Modern AI factories are highly interconnected and need to integrate networking, GPUs, storage, orchestration, and suites of applications. If configuration or integration issues appear in the deployed system, there will likely be major interruptions in the operation and costs associated with delays.

Many companies struggle with:

* A shortage of real-world physical lab testing.

* Long validation/deployment cycles.

* Discovering integration bugs at an inconvenient (read, costly!) late stage.

* The hidden post-deployment rework needed to fix.


That's where Aviz ONES on NVIDIA DSX Air helps deliver a better AI infrastructure build process: digital twin simulation. The idea is simple: instead of building, and then testing, why not design, simulate, and validate your entire AI networking environment beforehand?

Engineers can create a model of their production-scale network, simulate traffic from AI apps, and validate interactions between compute, networking, storage, and orchestration, all in a cloud-based simulation environment. Iterating on designs is faster, and risk goes down.

The outcome?

Infrastructure that's operational and tested Day 0. Repeatable deployments become a reality.

For systems integrators, it means no need for expensive testing labs; they can now hand off proven,

tested, validated solutions from the start.

With the scaling of AI environments, validation comes with simulation, but having a validate-first

approach means no matter how large your AI infrastructure is going to be, it's going to be Day-0 ready.

Explore the complete blog to learn how Aviz ONES and NVIDIA DSX Air enable validate-first AI

factory networking through full-stack digital twin simulation.

https://aviznetworks.com/resources/blogs/can-you-validate-ai-factory-

networking-before-deployment-instead-of-fixing-it-later


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