The network fabric behind AI performance
AI workloads expose the non-linear relationship between compute growth and network throughput. This visual explainer shows how congestion, east-west traffic, microbursts, and latency variance directly affect training efficiency and GPU utilization across modern leaf-spine fabrics.
Partner Solutions
With partners aligned through Avnet, the stack works together.
Avnet brings edge control, sensing & power, and connectivity/PoE expertise together then adds alternates and lead-time planning plus floor-ready kitting to protect your schedule. We work with a broad ecosystem (e.g. gateway, sensing, and connectivity leaders) in parity, selecting what fits your architecture and timeline.
Why network speed defines AI performance
GPU density, model size, and east-west traffic have pushed modern AI workloads into a regime where network behavior directly constrains training efficiency. This paper examines how throughput, congestion, and latency variance at the fabric level shape real-world AI performance inside today’s data centers.
7 things on the mind of an engineer
This article captures the core technical and professional pressures engineers face when designing modern AI and data center networks, from performance uncertainty and integration risk to reliability, technical debt, and long-term supportability. It provides real-world context for the architectural, performance, and sourcing decisions explored throughout this resource hub.
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Make, buy, or hybrid: choosing the right network ownership model under AI load
As AI workloads drive networks toward 400G and 800G, performance alone is no longer the deciding factor. This whitepaper presents a practical framework for evaluating OEM, white-box, and hybrid network models based on real-world constraints-latency sensitivity, east–west traffic, compliance, supply risk, and time-to-value.
Data center networks evolved rapidly over the past decade, driven by the need to handle growing data volumes efficiently while managing power, space and cost. Learn about the technologies making the advancements possible.
Pluggable optics have been the de facto choice in data center communication for years. However, in 2025, support for co-packaged optics grew. Power savings are a primary reason for change, but lower insertion losses are another potential benefit.
Within the data center, latency is a critical consideration for large-scale training and high-throughput inference setups. Leading AI training clusters are engineered for microsecond class communication.
Selecting network interface cards for AI data centers requires a balance of bandwidth, latency, off-load features and peripheral component interconnect express (PCIe) topology to optimize GPU cluster performance.
Validate your assumptions with a network specialist
For teams moving from evaluation to implementation, Avnet FAEs provide technical review across network topology, traffic behavior, and component selection to help reduce integration and performance risk.