NetApp, a global, cloud-led, data-centric software company, announced that NetApp EF600 all-flash NVMe storage combined with the BeeGFS parallel file system is now certified for NVIDIA DGX SuperPOD. The new certification simplifies artificial intelligence (AI) and high-performance computing (HPC) infrastructure to enable faster implementation of these use cases. Since 2018, NetApp and NVIDIA have served hundreds of customers with a range of solutions, from building AI Centers of Excellence to solving massive-scale AI training challenges. The qualification of NetApp EF600 and BeeGFS file system for DGX SuperPOD is the latest addition to a complete set of AI solutions that have been developed by the companies. NetApp's portfolio of NVIDIA-accelerated solutions includes ONTAP AI to eliminate guesswork for faster adoption by using a field-proven reference architecture as well as a preconfigured, integrated solution that is easy to procure and deploy in a turnkey manner.

NetApp, a global, cloud-led, data-centric software company, announced that NetApp EF600 all-flash NVMe storage combined with the parallel file system is now certified for NVIDIA DGX SuperPOD. The new certification simplifies artificial intelligence (AI) and high-performance computing (HPC) infrastructure to enable faster implementation of these use cases. Since 2018, NetApp and NVIDIA have served hundreds of customers with a range of solutions, from building AI Centers of Excellence to solving massive-scale AI training challenges. The qualification of NetApp EF600 and BeeGFS file system for DGX SuperPOD is the latest addition to a complete set of AI solutions that have been developed by the companies. "The NetApp and NVIDIA alliance has delivered industry-leading innovation for years, and this new qualification for NVIDIA DGX SuperPOD builds on that momentum," said Phil Brotherton, Vice President of Solutions and Alliances at NetApp.

NVIDIA has set the stage for businesses worldwide to design and deploy large language models (LLMs). This design enables them to develop domain-specific chatbots, personal assistants, and other artificial intelligence systems. The firm announced the NVIDIA NeMo Megatron framework for training trillion-parameter language models. In addition, NVIDIA Triton Inference Server offers multi-node distributed inference features for new domains and languages. When used in conjunction with NVIDIA DGX systems, these technologies provide an enterprise-grade solution for simplifying the construction and deployment of massive language models.

NVIDIA opened the door for enterprises worldwide to develop and deploy large language models (LLM) by enabling them to build their own domain-specific chatbots, personal assistants and other AI applications that understand language with unprecedented levels of subtlety and nuance. The company unveiled the NVIDIA NeMo Megatron framework for training language models with trillions of parameters, the Megatron 530B customizable LLM that can be trained for new domains and languages, and NVIDIA Triton Inference Server with multi-GPU, multinode distributed inference functionality. Combined with NVIDIA DGX systems, these tools provide a production-ready, enterprise-grade solution to simplify the development and deployment of large language models. "Large language models have proven to be flexible and capable, able to answer deep domain questions, translate languages, comprehend and summarize documents, write stories and compute programs, all without specialized training or supervision," said Bryan Catanzaro, vice president of Applied Deep Learning Research at NVIDIA. "Building large language models for new languages and domains is likely the largest supercomputing application yet, and now these capabilities are within reach for the world's enterprises."

You can't be first if you're not fast. Inside the world's top companies, teams of researchers and data scientists are creating ever more complex AI models, which need to be trained, fast. And that's why the AI training results released today by MLPerf matter. Across all six of six MLPerf categories, NVIDIA demonstrated world-class performance and versatility. Our AI platform set eight records in training performance, including three in overall performance at scale and five on a per-accelerator basis.