Rack-scale networking is becoming essential for massive AI workloads, offering significantly higher bandwidth compared to traditional scale-out networks like Ethernet and InfiniBand. Companies like Nvidia and AMD are leading the charge with advanced architectures that facilitate pooling of GPU compute and memory across multiple servers, catering to the demands of large enterprises and cloud providers. These systems, while complex and expensive, are designed to handle increasingly large AI models and their memory requirements.

The article discusses the concept of fair queueing, a method used in computer networking to ensure that resources are allocated fairly among users. It explains how fair queueing helps manage bandwidth and latency by prioritizing traffic based on specific algorithms, promoting equitable access to network services. The piece also highlights its significance in improving overall network performance.