TLDR: The next AI-networking bottleneck is not only switch bandwidth. At 800G and 1.6T-class links, the power, signal integrity and reliability cost of moving bits between the switch ASIC and front-panel optics becomes part of the system design. Co-packaged optics moves the optical engines much closer to the switching silicon, making watts per bit and optical reliability first-class AI-fabric concerns.
TLDR: Broadcom's Thor Ultra 800GbE NIC shows where AI Ethernet is heading: packet-level multipathing across as many as eight planes, out-of-order placement directly into accelerator memory, selective retransmission, and programmable congestion control. The important shift is that the NIC is becoming an active participant in fabric utilization rather than treating the network as a single ordered path.
TLDR: NVIDIA reported another record networking quarter, with networking revenue growing 18% sequentially and Spectrum-X Ethernet growing 2.6x year over year. The important story is not simply that NVIDIA is selling more switches. AI infrastructure is forcing compute, NICs, congestion control, topology, optics and collective communication to be engineered as one system — and NVIDIA is capturing more of that system with every generation.
TLDR: You can build the same logical AI fabric on five different vendor stacks — Spectrum-X, Tomahawk, Jericho3-AI + DDC, Cisco Silicon One G200, and Arista EOS. They are not interchangeable. This is the procurement view: what each vendor sells, who actually buys it, and the side-by-side comparison you'll do at RFP time.
TLDR: New site, built for network engineers entering AI. Deep modules + fast blog + zero vendor noise. First module drops soon.
Welcome to Lossless Network — AI networking, distilled for network engineers.
If you've ever tried to design or operate a network fabric for large-scale AI training, you've felt the gap. The standards are public. The vendor whitepapers exist. But nowhere is there a single, opinionated, technically honest walkthrough of how the pieces fit together — written by a network engineer, for network engineers — and why some choices that look right on paper fall apart at scale.