It includes the fact that we are architecturally compatible across every single one of those. It includes all of the domain specific libraries that we create. The reason why every computer company, without thinking, can integrate NVIDIA into their roadmap and take it to market. And the reason for that is, because there is market demand for it. There is market demand in healthcare, there is market demand in manufacturing, there is market demand, of course, in AI, including financial services, in supercomputing and quantum computing. The list of markets and segments that we have domain specific libraries is incredibly broaden. And then finally, we have an end-to-end solution for data centers; InfiniBand networking, Ethernet networking, x86, ARM, just about every permutation combination of solutions — technology solutions and software stacks provided.
And that translates to having the largest number of ecosystem software developers; the largest ecosystem of system makers; the largest and broadest distribution partnership network; and ultimately, the greatest reach. And that takes — surely that takes a lot of energy. But the thing that really holds it together, and this is a great decision that we made decades ago, which is everything is architecturally compatible. When we develop a domain specific language that runs on one GPU, it runs on every GPU. When we optimize TensorRT for the cloud, we optimized it for enterprise. When we do something that brings in a new feature, a new library, a new feature or a new developer, they instantly get the benefit of all of our reach. And so that discipline, that architecture compatible discipline that has lasted more than a couple of decades now, is one of the reasons why NVIDIA is still really, really efficient.
I mean, we’re 28,000 people large and serving just about every single company, every single industry, every single market around the world.
Operator: Thank you. I will now turn the call back over to Jensen Huang for closing remarks.
Jensen Huang: Our strong growth reflects the broad industry platform transition from general purpose to accelerated computing and generative AI. Large language models start-ups consumer Internet companies and global cloud service providers are the first movers. The next waves are starting to build. Nations and regional CSPs are building AI clouds to serve local demand. Enterprise software companies like Adobe and Dropbox, SAP and ServiceNow are adding AI copilots and assistants to their platforms. Enterprises in the world’s largest industries are creating custom AIs to automate and boost productivity. The generative AI era is in full steam and has created the need for a new type of data center, an AI factory; optimized for refining data and training, and inference, and generating AI.
AI factory workloads are different and incremental to legacy data center workloads supporting IT tasks. AI factories run copilots and AI assistants, which are significant software TAM expansion and are driving significant new investment. Expanding the $1 trillion traditional data center infrastructure installed base, empowering the AI Industrial Revolution. NVIDIA H100 HGX with InfiniBand and the NVIDIA AI software stack define an AI factory today. As we expand our supply chain to meet the world’s demand, we are also building new growth drivers for the next wave of AI. We highlighted three elements to our new growth strategy that are hitting their stride: CPU, networking, and software and services. Grace is NVIDIA’s first data center CPU. Grace and Grace Hopper are in full production and ramping into a new multi-billion dollar product line next year.
Irrespective of the CPU choice, we can help customers build an AI factory. NVIDIA networking now exceeds $10 billion annualized revenue run rate. InfiniBand grew five-fold year-over-year, and is positioned for excellent growth ahead as the networking of AI factories. Enterprises are also racing to adopt AI and Ethernet is the standard networking. This week we announced an Ethernet for AI platform for enterprises. NVIDIA Spectrum-X is an end-to-end solution of Bluefield SuperNIC, Spectrum-4 Ethernet switch and software that boosts Ethernet performance by up to 1.6x for AI workloads. Dell, HPE and Lenovo have joined us to bring a full generative AI solution of NVIDIA AI computing, networking and software to the world’s enterprises. NVIDIA software and services is on track to exit the year at an annualized run rate of $1 billion.
Enterprise software platforms like ServiceNow and SAP need to build and operate proprietary AI. Enterprises need to build and deploy custom AI copilots. We have the AI technology, expertise and scale to help customers build custom models with their proprietary data on NVIDIA DGX Cloud and deploy the AI applications on enterprise grade NVIDIA AI Enterprise. NVIDIA is essentially an AI foundry. NVIDIA’s GPUs, CPUs, networking, AI foundry services and NVIDIA AI Enterprise software are all growth engines in full throttle. Thanks for joining us today. We look forward to updating you on our progress next quarter.
Operator: This concludes today’s conference call. You may now disconnect.
