Network
The painful choice between proprietary performance and open economics is over. The fabric schism has ended. As performance equalizes, the overwhelming TCO and strategic advantages of open Ethernet make it the only logical path forward for AI at scale.
Network
How do you actually build a 100,000-GPU cluster? This blueprint outlines a practical reference architecture: a hybrid model using proprietary links for scale-up and open UEC Ethernet for the massive scale-out, all tied together by the DPU.
Network
The most expensive network isn't always the best. As open Ethernet closes the performance gap, the real math comes down to Performance-Adjusted TCO. When speed is equal, the lower cost of hardware and people becomes the deciding factor.
Network
The battle for AI networking is a clash of philosophies. NVIDIA's integrated fortress offers turnkey performance at a proprietary cost. The Open Alliance bets on collaboration and open standards. Your choice is more than tech—it's a strategic vote.
Network
Your multi-million dollar GPUs are wasting time on chores. The DPU is the third pillar of compute, a specialized processor that offloads infrastructure tasks, freeing up your CPU/GPU and enabling the entire business model of the secure AI cloud.
Network
Traditional networks create traffic jams. Ultra Ethernet's secret weapons are packet spraying and flexible order—a new design that eliminates hotspots and unlocks the full power of the fabric by breaking all the old rules of packet delivery.
Network
Standard Ethernet can't handle AI. Early attempts were brittle patches on a flawed foundation. Now, a massive industry alliance is re-architecting it from the ground up. This is the story of the Ultra Ethernet Consortium's revolution.
Network
For over two decades, InfiniBand has powered the fastest AI clusters through native lossless design, RDMA, and in-network computing—making it the performance king. Yet, NVIDIA’s tight integration and high costs have hyperscalers seeking open, Ethernet-based alternatives.
Data Center
AI breakthroughs aren’t just about GPUs—networks are the real bottleneck. Large-scale AI training jobs depend on rapid, lossless communication; even a 0.1% packet loss can slash GPU utilization by 13%. Job completion time now depends on robust, predictable network design.