“Data center compute revenue nearly tripled year on year driven primarily by accelerating demand for cloud from cloud service providers and large consumer internet companies for our HGX platform, the engine of generative and large language models,” said Colette Kress, Executive Vice President and Chief Financial Officer of Nvidia, on the conference call.It’s not just about GPUs.
Just to give you the sense of scale: A single HGX A100 system has 1.3 terabytes of GPU memory and 2 terabytes/second of memory bandwidth. The storage handles 492 of SSDs and external networking capacity is 400 gigabits/s.Don’t expect Nvidia to stop filling extending the AI stack. It’s been steadily making key acquisitions targeting AI systems to build HGX. In 2022, it acquired Excelero for block storage systems and Bright Computing to drive high performance compute clusters.
“So, this runtime called NVIDIA AI Enterprise has something like 4,500 software packages, software libraries and has something like 10,000 dependencies among each other,” explained Huang on the call. “And that runtime is — is, as I mentioned, continuously updated and optimized for — for our install base for our stack.
“The AI opportunity is exciting,” said Arista CEO Jayshree Ullal. “As our largest cloud customers review their classic cloud and AI networking plans, Arista is adapting to these changes, thereby doubling down on our investments in AI.”on its earnings conference call a week ago. Cisco is releasing upgraded Ethernet switches with a new line of Cisco Silicon One ASICs designed to compete with AI networking systems based on NVIDIA’s InfiniBand.