Aviz Networks outlines network design priorities for AI era costs

The vendor blog argues that AI progress depends on networking design as much as on compute, citing multi-network architectures, open standards, and vendor-agnostic planning to control AI operating costs. For enterprise IT and security leaders, this reframes network strategy as a core factor in scalability and ROI planning for AI factories.

Research Overview

The post centers on a discussion about why AI infrastructure shifts the role of networking from background connectivity to a performance and scaling dependency. It describes how AI factories and AI fabrics change the workload mix and make network decisions relevant to longer-term return on investment.

It also frames networking as part of an ecosystem that includes GPUs, storage, and software layers coming from multiple sources. The blog presents open and standardized networks as the integration layer needed to connect those components.

Key Findings

The blog states that AI workloads require coordination among GPUs, storage, and applications, which increases the importance of networking for performance and scale. It characterizes this as a change from earlier “invisible infrastructure” expectations.

It outlines network requirements as scale-up, scale-out, and hybrid configurations supporting training, inference, and traditional business workloads. It further links network rigidity or vendor lock-in to rising costs and slower adaptation of AI systems.

Technical Breakdown

The post provides a “mini framework” for how networking layers map to workload purpose. It describes scale-up networks for high-speed GPU-to-GPU communication, scale-out networks for data center-wide connectivity, and traditional networks for business applications and legacy systems.

It argues that AI introduces multi-network environments and that a single network cannot efficiently address GPU communication, data center traffic, and traditional application needs at the same time. It positions multi-network architectures as the approach to handle these separate traffic and performance domains.

Operational Impact

The blog describes an AI cost model based on “cost per token (or AI output)” and says network design affects whether those costs scale efficiently or expand over time. It ties vendor lock-in to increased long-term costs, innovation slowdowns, and accumulating technical debt.

It also states that standardized and open networks enable faster hardware adoption, continuous price-performance improvement, and predictable serving costs. To support ROI-driven design, the post includes evaluation questions such as whether the network is vendor-agnostic, can integrate new compute like GPU/XPU, supports training and inference, and allows costs to scale linearly rather than exponentially.

Leadership Perspective

The post counters the view that AI networking investment can wait until GPUs are deployed, saying application architectures are already shifting toward AI. It describes waiting as leading to catch-up work, higher costs, and rushed decisions as AI requirements develop.

For adoption planning, it recommends engaging with the ecosystem, defining a future-state AI-first architecture, starting with pilots in test environments, moving toward standardization, and gradually transitioning to open systems. The conclusion emphasizes that networking is a core enabler of AI, that multi-network architectures replace traditional single-design approaches, and that openness and standardization drive ROI.

The overall takeaway is that the blog treats network architecture as a central dependency for AI scalability, cost control, and integration across an ecosystem of GPUs, storage, and software. It frames enterprise planning around multi-network designs, open and standardized architectures, and early pilot work to maintain predictable AI operating costs. Blog Signals brief is a fact-based summary of the vendor blog.