Aviz Podcast Episode 2 outlines AI networks for AI factories
60th article in the last 90 days, one of 487 articles referencing Aviz Networks. Previous coverage: Aviz Networks podcast details AI networking shift and KPI changes (Apr 2026).
Companies mentioned
Best suited for
- Seniority
- Director
- Job function
- Network / Network Architect
- Persona
- Network Architect
- Buyer role
- Architect / Technical Evaluator
- Decision stage
- How to start
- Adoption curve
- Early Majority
- Technology maturity
- Market Correction
- Industry
- Information Technology / Software & Services / IT Services / Cloud Services
Our classification, not the publisher's statement. Best suited for, not only for.
The vendor blog argues that AI infrastructure needs network designs treated as a core scaling layer, shifting organizations from one fabric to multiple coexisting fabrics for training and inference while managing higher complexity. Enterprise IT and security leaders are asked to plan early for openness and cost models that can affect long-term ROI.
Research Overview
The post presents a discussion with Vishal Shukla and Alan Weckel on building “AI factories” and “AI fabrics” and on how infrastructure decisions should change for the AI era. It frames the update around market evolution and the decisions leaders can make before AI capacity ramps.
The blog ties networking to scale as AI workloads expand, citing GPU and XPU use as drivers of new infrastructure requirements. It also characterizes data center networking growth as expected alongside increased complexity.
Key Findings
The blog says networking moves beyond connectivity to become a unifying layer that enables AI scale. It describes a shift from a single network to multiple networks to support AI factories.
It also links that shift to multiple workload types running together, including training, inference, and traditional applications. The post asserts that these patterns increase the operational burden on infrastructure teams compared with prior approaches.
Technical Breakdown
According to the blog, AI networks differ from traditional networks by supporting multiple coexisting designs for different compute needs. It describes training and inference compute as distinct from workloads for traditional applications, requiring multiple architectures to operate on a shared fabric.
The post contrasts the change with prior waves such as software-defined networking or disaggregation, stating that AI is changing how networks are built and operated from the ground up. It also explains that organizations may need separate fabrics for training, inference, and traditional workloads, each with different performance and latency requirements.
Operational Impact
The blog emphasizes that leaders should pursue open and standardized networks to manage long-term ROI and reduce vendor lock-in. It says standardization can support faster adoption of new hardware and improve price performance as the ecosystem evolves.
It highlights cost modeling as a near-term planning factor, naming token serving cost and application cost as metrics that will become important as AI adoption grows. It further states that preparing only after GPUs arrive creates risk because applications and their network requirements are already shifting.
The overall takeaway is that the blog frames networking as a strategic layer for scaling AI workloads, with organizations expected to plan for multiple fabrics, higher complexity, and cost model effects. It argues that early engagement, openness, and standardization decisions matter to scalability and cost efficiency. This “Blog Signals brief” is a fact-based summary of the vendor blog.
Blog post, originally published by Yohan Kattackel Bobby at Aviz Networks.