Nvidia is more than just a leader in graphics and AI hardware. It has also become a titan of the networking industry with bigger datacenter Ethernet sales than any other vendor. In Q2, Nvidia’s networking revenues topped $17. 5 billion. To put that in perspective, Cisco’s revenue during that same period, across all products, was $17. 3 billion. GPUzilla’s arrival as a networking power feels sudden, yet Jensen Huang started working towards this status in 2019 with the $6. 9 billion acquisition of long-time networking partner Mellanox . At the time, Mellanox wasn’t a big name in Ethernet switching. The company was mostly known for its ConnectX NICs and InfiniBand switches. Outside of high-performance computing (HPC) and supercomputing clusters, and a big deployment at Microsoft, InfiniBand wasn’t widely deployed in hyperscale datacenters. Buying Mellanox allowed Nvidia to reposition itself as a system builder, which means something different in the context of distributed compute. Nvidia understood its datacenter customers weren’t interested in buying GPUs or even servers; they wanted clusters of systems. For that, Nvidia needed interconnect and Mellanox’s InfiniBand fit the bill. Relentless co-design
Over the years, Huang has talked about co-design – designing components and software to work together for specific set of jobs rather than to be the best GPU, switch, or NIC. Its work with Mellanox is a prime example of that approach. According to Dell’Oro Group analyst Sameh Boujelbene, Nvidia’s decision to integrate compute and networking became key to the company’s success. "Customers choose Nvidia for its tightly integrated compute and networking architecture, optimized for AI workloads," she told The Register . That integration makes deployments easier and speeds time to market. Ben Mabey, CTO of pioneering AI and machine learning company Recursion, told The Reg Nvidia’s integration schemes won him over. Founded in 2013, nearly a decade before ChatGPT made AI the subject of dinner-table discussion, Recursion sought to apply machine learning running atop GPUs to the problem of drug discovery. This work involved churning through massive datasets which, Mabey tells us, now total more than 50 petabytes in size. "When you think about our workloads, it makes LLMs workload training seem easy compared to the amount of data that has to be moved around," he told The Register . "In the biological space and sciences in general, it’s not well appreciated just how big the data actually is," he added. "The internet’s big, but in the grand scheme of things, the text that people are training on is actually pretty small, versus the petabytes of data that we have of these images." Recursion’s first cluster was a system called BioHive Zero which it cobbled together from GTX 1080 graphics cards originally intended for gaming systems. It used Ethernet to connect GPUs and ran into the limits of conventional networking. So, for its second cluster, an A100-based machine with around 300 accelerators, Recursion went with Nvidia’s Quantum InfiniBand switches and ConnectX NICs. As Mabey explains it, buying its compute and interconnects from the same vendor vastly simplified optimization and troubleshooting. When Recursion reached out to Nvidia for help tuning the system, they were able to consider it as a single system, rather than discrete compute and networking resources. This level of integration had only previously been offered by boutique supercomputer builders like Cray or Atos (Eviden/Bull/whatever it gets renamed next). A wider Spectrum
Nvidia hasn’t always listed networking revenue as a single line item in its financials. However, based on CFO commentary and a little back of the napkin math we can fill in the blanks. Nvidia’s networking revenues have grown steadily since the Mellanox acquisition. At two moments, its revenue shot up suddenly. The first came in the 2023 calendar year (FY24) after OpenAI kicked off the AI gold rush with the launch of ChatGPT. The second spread across 2024 and 2025, when Nvidia started selling Blackwell-generation GPUs and then started to ship them in large quantities…