The real bottleneck of AI transformation is network infrastructure

AI workloads are changing the structural nature of network traffic, rendering existing statistical multiplexing principles ineffective
To prevent straggler effects caused by packet drops in distributed learning, lossless Ethernet based on RoCEv2 or InfiniBand is essential
Inference traffic has flow durations more than twice as long as web traffic, and upstream traffic reaches approximately 9% due to prompts including context
Influenced by Data Gravity, the architecture is evolving toward terabit-scale DCI and intelligent WAN routing for edge inference
Infrastructure performance metrics are shifting from simple bandwidth to minimizing Job Completion Time (JCT)

AI Traffic Characteristics

During the data ingestion phase, transmitting tens to hundreds of TB immediately saturates 10G/40G lines
In the inference phase, session persistence causes cumulative bottlenecks in firewall and IDS flow tables
When applying RAG, jitter control is required due to the intersection of vector DB lookups and token streaming

Strategies to move compute resources to data locations are emerging due to increased data movement costs
Terabit-scale optical communication backbones of 400G to 800G or higher between data centers, with MACsec encryption applied
Advanced development of latency-free intelligent routing protocols for model weight synchronization to edge nodes