Aviz Networks details AI factory production requirements
2 companies named across 6 categories, one of 850 articles referencing Aviz Networks. Previous coverage: Aviz Networks and MITSUI KNOWLEDGE INDUSTRY CO., LTD outline open networking (Oct 2026).
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- IT Infrastructure
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- Platform / Infrastructure Architect
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- Information Technology / AI Infrastructure / AI Compute & Systems / AI Servers & Rack-Scale Systems
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The latest episode of Aviz Networks’ podcast examines what it takes to move AI factory concepts from lab settings into production use. The discussion centers on system integration, automation, and reliability concerns that enterprise teams face when scaling AI infrastructure.
Research Overview
The episode features Nicolas Kremer of Polarise, David Iles of NVIDIA, and Lukas Gentele of vCluster in a discussion with Vishal Shukla and Ilona Gabinsky of Aviz Networks. The conversation focuses on the conditions needed for an AI factory to function as a production system rather than a demonstration environment.
Key Findings
David Iles said an AI factory is not production-ready until compute, network, storage, security, and operations have been validated together as one system. He said the system must be deployable in a predictable way, operate reliably at scale, and accept changes without putting production at risk.
He also said the value of an AI factory is measured differently from a traditional data center. In this model, productive GPU time and token generation are the main metrics.
Technical Breakdown
Kremer said scaling becomes more complex when a solution must run across 10,000 nodes rather than in a lab. He said NeoCloud providers need partners to deliver scale and quality across the full stack.
Gentele said vCluster focuses on the software layer after the factory, networking, and storage are already in place. He said automation at that layer is needed so recovery can happen in an orderly and predictable way, including during a grid outage.
Network and operating model
Shukla said predictable network automation is needed to support multi-tenancy at scale. He said reference architectures define how multi-tenancy should work as environments move from scale up to scale out and scale across.
He said automation helps keep those designs manageable once they are in operation. The episode also notes that definitions of multi-tenancy can differ across storage vendors, NeoClouds, and enterprise customers.
Leadership Perspective
Each guest offered advice for teams building AI factories. Iles said teams should use proven reference architectures and can try NVIDIA DSX Air, where pre-built AI factories can be started in minutes.
Kremer said teams should not avoid breaking things, since AI infrastructure is still being learned and operated at scale. Gentele said teams should automate, test, and assume failure, while Shukla described a framework built around process, people, and a predictive platform.
The full episode covers additional discussion of how multi-tenancy is defined across different vendors and customers. This Blog Summary is a fact-based summary of the vendor blog and is relevant for enterprise leaders evaluating how to move AI infrastructure from simulation into production.
Blog post, originally published at aviznetworks.com.