“Our networking products are still supply constrained though we expect continued improvement throughout the rest of the year.”

In context

“GPU for both training and inference with large volume deployments by hyperscale customers and broadening adoption across the vertical industries. Top workloads includes recommender systems, conversational AI, large language models and cloud graphics. Networking revenue accelerated on strong broad-based demand for our next-generation 25, 50 and 100-gig ethernet adapters. Customers are choosing NVIDIA's networking products for their leading performance and robust software functionality. In addition, networking revenue is benefiting from growing demand for DGX super pods and cross-selling opportunities. Customers are increasingly combining our compute and networking products to build what are essentially modern AI factories with data as the raw material input and intelligence as the output. Our networking products are still supply constrained though we expect continued improvement throughout the rest of the year. One of the biggest workloads driving adoption of NVIDIA AI is natural language processing, which has been revolutionized by transformer based models. Recent industry breakthroughs traced to transformers include; large language models like GPT-3, NVIDIA Megatron BERT for drug discovery and DeepMind AlphaFold for a protein structure prediction. Transformers allow self-supervised learning without the need for human labeled data. They enable unprecedented levels of accuracy for TAF such as text generation, translation, summarization and answering questions. To do that, Transformers use enormous training data sets and very large networks well into the hundreds of billions of parameters. To run these giant models without sacrificing low inference times, customers like Microsoft are increasingly”